Multiple sensor expert system for diagnostic reasoning, monitoring and control of mechanical systems

Mechanical Systems and Signal Processing (1988) 2(2), 165-185

MULTIPLE SENSOR EXPERT SYSTEM FOR DIAGNOSTIC REASONING, M O N I T O R I N G A N D C O N T R O L OF M E C H A N I C A L SYSTEMS ALICE M. AGOG1NO AND SAMPATH SRINIVAS

Department of Mechanical Engineering, Intelligent Systems Research Group, University of California, Berkeley, California, U.S.A. AND KENNETH M. SCHNEIDER

Integrated Systems, Inc., Santa Clara, California, U.S.A. (Received November 1987, accepted February 1988) This paper describes an expert systems architecture for integrating multiple sensors for diagnostic reasoning, monitoring and supervisory control of mechanical systems in automated manufacturing and process control. The IDES (Influence Diagram based Expert System) performs probabilistic inference and expected value decision making. It integrates dynamic sensor readings, statistical data and subjective expertise in symbolic and numerical data structures and is designed for real time performance. An application using acoustic, current and force sensors on a numerically-controlled milling machine is described. In this example, the fusion of information from multiple sensors achieves effective prediction and control performance with relatively simple signal processing. 1. INTRODUCTION In-process diagnostics and monitoring of mechanical systems requires reasoning about failure and process states from sensor readings. Often the relationship between the sensor readings and the process states is complex and non-deterministic. The diagnostician's problem is to determine the most likely process state from the observable sensor readings. In the unattended milling machine problem described in this paper, this corresponds to determining the state of the cutting process ("normal", "worn tool", "broken tool" or "tool chatter") given the observables (current, dynamometer and acoustic sensor readings). The controller's problem is to determine the optimal course of action given these observables and process goals. Within our example, this means modifying the milling control so as to prevent tool, workpiece or machine damage. In this paper, we introduce an expert systems architecture for integrating dynamic sensor readings, statistical data and subjective expertise for solving both the inference and control problems in computer-integrated mechanical systems requiring real time performance. 2. EXPERT SYSTEMS: PROS AND CONS Medical diagnostics led the way in successful applications of expert systems (e.g. M Y C I N [1], C A D U C E U S [2] and PUFF [3]). Recently, the engineering community has applied this new technology to automated diagnostic reasoning and computer-based automated manufacturing (e.g. copier diagnostics [4], NC-Consultant [5], Polyfill [6], M I C R O P L A N [7], IDM [8], FAXS [9], DART [10], and Delta [11]). Although many 165 0888-3270/88/020165+21 $03.00/0 O 1988 Academic Press Limited

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of the same techniques can be applied, mechanical diagnostic and manufacturing systems have requirements above those normally found in medical diagnostics. To be of value, a model of the physical equipment and system interrelationships involved may be required. In addition, manufacturing and process environments typically require relatively short response rates. 2.1. ADVANTAGES OF EXPERT SYSTEM TECHNOLOGY Human beings possess an unparalleled ability for processing complex types of information and making inferences. Expert diagnosticians and equipment operators, whose knowledge is not easily encoded by numerically based algorithms for control, are often more efficient than present automated systems [ 12]. Yet much of the diagnostic equipment today does not tap this tremendous resource. Unfortunately, our abilities are limited. The technical complexities of our world have extended beyond the realm where we can effectively integrate large amounts of critical information without some kind of automated assistance. The need for automated diagnostic tools is even more acute in manufacturing applications because of the short time requirements tO process data and effectively respond. 2.2.

LIMITATIONS

OF CURRENT

EXPERT SYSTEM TECHNOLOGY

Although there have been a number of new products in the commercial market, current expert systems tools and theoretical methodologies are inadequate or limited for many mechanical diagnostic and manufacturing applications [13]. The widely accepted approach in the artificial intelligence community is to use production rules based on predicate calculus and classical logic. A typical production rule is of the form: antecedents ~ consequences. If encoded in a language capable of symbolic manipulation, the diagnostic expert system can be designed to chain rules together in order to make inferences based on classical logic. A separate control system selects which rules to evaluate or combine with other rules. If this control mechanism is independent of the knowledge base, it can be a simple operation to add new rules or modify old rules in the knowledge base. A major obstacle to knowledge representation in classical binary logic is its inability to handle competing or non-deterministic rules. When problems involve failures and uncertain events and parameters, as in engineering diagnostics, the knowledge is seldom of a binary nature, and thus classical approaches to expert systems may be inadequate. Multivariate logics such as probability measures may be needed. The rapid advances in sensor technology (e.g. ultrasonic testing, vision systems, etc.) beg the issue of how to most effectively fuse this diverse data and link to data bases in automated manufacturing applications, recognising that the sensor information is neither perfectly accurate nor complete. Production rules based on binary logic have trouble taking into account imperfections in the measuring instruments. Simple binary rules are prone to making false-positive and false-negative predictions. It is not surprising that spurious sensor readings and sensor degradation over time are often reported as a problem in rule-based systems [14], [15]. To overcome the difficulty of incorporating probabilistic knowledge in binary logic, the rules are sometimes weighted by the degree of belief in the rule. For example, in the medial diagnostic system MYCIN, the knowledge is stored in rules of the form " I f Ei then D / ' where Ei represents evidence that supports disease hypothesis D r. CF(Dj, E~) is the certainty factor for the rule and ranges between - 1 and 1. Positive certainty factors are a quantitative measure of relative (not absolute) support for the hypothesis and

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negative numbers measure the relative support against the hypothesis. C F ( Di, E i )

El

) Dr

When multiple pieces of evidence support one disease hypothesis, the certainty factors are combined to give a joint factor in a way that does not take into account any dependencies between the evidence. If analogies are made to probability theory, Heckerman and Horvitz [16] have shown that the MYCIN function for combining evidence is analogous to Bayes' formula assuming that each knowledge rule is conditionally independent. Although there may be practical justification for these kinds of simplifying assumptions, it is at the expense of theoretical rigor and robustness [17]. One critical deficiency in a simple weighting of rules is apparent in the knowledge acquisition phase of building expert systems. In order to maintain the ability to add new rules to the knowledge base without altering other rules or the control structure, conditional and joint dependencies concerning other rules are not considered. Unfortunately, these joint and conditional dependencies can be critical aspects of diagnostic reasoning. Probabilistic influences more often than not are highly interdepende~it. 2.3. I N F L U E N C E D I A G R A M E X P E R T S Y S T E M S A P P R O A C H There is growing support in the artificial intelligence research community for the use of influence diagrams for representing uncertain knowledge in building expert systems [ 16], [ 18]-[22]. Influence diagrams provide an intuitive graphical framework for representing complex interdependencies and rules for combining evidence that are based on rigorous probability theory. The IDES (Influence Diagram based Expert System) developed at the University of California at Berkeley [23] is an implementation of influence diagram technology with efficient algorithms designed for real time applications. 3. UNATTENDED MILLING MACHINE PROBLEM Unattended manufacturing has all of the attributes that challenge conventional rulebased expert systems: multiple sensors that require some level of signal processing, imperfect or uncertain mapping between sensor readings and critical states, complex interdependencies, and real time response needs. The unattended milling machine problem will be used as an example to demonstrate the strength of the IDES approach under these challenging conditions. A review of previous work with current in-process sensor technologies applied to milling applications will be provided followed by an introduction to the theory of influence diagrams and their application to the unattended milling machine problem. Various sensors and signal processing techniques have been investigated for application to unattended machining. A brief review of some of the current approaches to in-process monitoring using dimension, force, current, acceleration and acoustic emission sensors follows. 3.1. D I M E N S I O N S E N S I N G Dimensional sensors can be of contact or non-contact type. They are highly suitable for dimensional and orientation checks of blanks or machined surfaces and in detecting thermal drifts, but they are of limited utility in detecting tool wear, tool breakage and chatter [24]. 3.2. MONITORING OF MOTOR CURRENT The motor current is directly related to the power required by the milling machine which is in turn related to the total torque on the motor shaft. The loads that influence

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the current include those associated with worn or broken tools, misaligned components, and faulty lubrication systems [25]. The current drawn during "air cutting" is a measure of the power required to overcome internal friction. By analysing increases in the current over these values cutting forces can be estimated and inferences about the machine state can be drawn. Stein and Shin [25] have developed a model for relating changes in the current to changes in the tool force. They observe that the motor current technique works better at high spindle speeds and large cutting forces, conditions that one may expect in modern day machining [26]. It is noted that the technique involves a trade-off between achieving high sensitivity, acceptable bandwidth and good transient responses. Measurement of motor current has the advantage that the sensors are nonintrusive, inexpensive and easy to incorporate and maintain. One disadvantage is that the large rotational inertias in the drive train have a capacitive effect which can cause a significant attenuation of the signal component related to cutting force at the tool chip interface. Therefore moderate or rapid variations in the cutting forces can be hard to detect making the mapping from the current measurement to machine state imprecise. Further the 60 Hz noise present due to the line voltage adds additional uncertainties in the signal processing. 3.3. MONITORING OF TOOL FORCES USING A DYNAMOMETER Altintas et al. [27] discuss an algorithm for detecting tool breakage using force averaging and normalised differencing. A problem identified is that transients generated by the start and end of cut are not easily distinguished from actual tool breakage. Lan and Naerheim [28] discuss an adaptive signal processing scheme for detecting tool fracture and chipping in milling which is reported to be better at distinguishing actual tool breakage from transients due to other causes. The problem with dynamometers is that they are intrusive. The work is mounted on the dynamometer, an awkward arrangement in many manufacturing environments. On the other hand, of all sensed quantities, cutting force has the most direct bearing on the process state. 3.4. VIBRATION MONITORING US1NG ACCELEROMETERS Braun et al. [29] describe a signal processing scheme aimed at monitoring tool wear development in single tooth milling using an accelerometer as the sensor. A signal generation model based on cyclostationary (periodically stationary) processes is developed and applied to extract features from the signal. They demonstrate that it is possible to specify a classification strategy for wear dependent only on an energy threshold. It is noted that the technique was applied to a specific kind of interrupted cutting and that different solutions will be required for other operations. 3.5.

ACOUSTIC

EMISSION

MONITORING

Acoustic emission (AE) sensors have recently been applied to diagnostics in machining. Acoustic emission from a machining process is caused by the energy that is released as the material's microstructure changes and shows a clear correlation with various process variables and events such as wear and fracture. It is non-intrusive and is easily installed [30]. Dornfeld [31] lists four major sources of acoustic emission in metal cutting: (1) plastic deformation of the workpiece in the shear zone, (2) plastic deformation and sliding friction between chip and tool rake surface, (3) sliding friction between workpiece and tool flank surface, and (4) collision, entangling and breakage of chips. Additional sources in face milling include the shock wave generated in tool entry and the sudden unloading and chip break off at tool exit. Dornfeld [31] has successfully used time difference averaging techniques for analysing the AE signal. The peak value of the acoustic emission

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strongly correlates with a parameter developed for fracture. The acoustic emission RMS is also shown to increase with another parameter developed to characterise tool wear. A theoretical model to describe the AE signal caused by tool fracture has been proposed by Diei and Dornfeld [32]. They show that the signal amplitude associated with fracture is significantly more than that associated with tooth engagement and disengagement and thus the peak of the AE signal can be used as a indicator of tool fracture. 3.6. T H E N E E D F O R S E N S O R F U S I O N These studies reflect the progress made in the last decade towards developing unattended computer-integrated manufacturing systems. However, they also reveal practical limitations that restrict the full utilisation of these sensor technologies today. In summary: 1. There are no simple deterministic models relating the state of the tool chip interface to sensor readings. All of the models have varying degrees of uncertainty associated with them. How should machines be programmed to respond to this "noisy" information ? 2. No single sensor is clearly preferred over the others. Each has specific advantages and disadvantages depending on the operation and the environmental conditions. How can diverse sensor readings be "fused" in order to make intelligent inference and control decisions ? 3. Most of the approaches to date use a single sensor for use in techniques that require extensive signal processing and are only applicable to specific machining conditions. How can these techniques be generalised to a broader class of operations without adding more burden to the already heavy computational requirements in signal processing? 4. Manufacturing researchers, engineers, and operators possess both first-principle and experimental knowledge concerning the interpretation of this sensory data. How can this subjective and often qualitative information be utilised in intelligent automated manufacturing systems ? In this paper, influence diagrams are proposed as a framework for integrating operator's expertise, first-principle knowledge and experimental data for the wide range of sensors possible for intelligent in-process monitoring and control. The use of multiple sensors reduces the sensitivity of the system to any particular sensor's drawbacks, requiring less precision than needed with a single sensor and thus potentially requiring less sophisticated signal processing. The non-deterministic nature of the inference problem and "noisy" sensor data is handled by operations with Bayesian probability. In the IDES implementation, features are extracted from the raw sensor data and used by the expert system to answer specific queries and send control signals out to the machine operator or controller. 4. INFLUENCE DIAGRAMS Influence diagrams were developed to facilitate automating the modeling of complex decision problems involving uncertainty [33]. The SRI researchers found that influence diagrams provided a compact graphical framework for representing the interrelationships and flow of information between the variables involved in the problem under consideration. A brief discussion of the mechanics of influence diagrams and the motivation behind them follows. For a fuller understanding of the theoretical and modeling aspects of influence diagrams, interested readers are referred to Rege and Agogino [14], Agogino and Rege [23], Howard and Matheson [34], Olmsted [35], Shachter [36], [37] and Henrion

[20]. An influence diagram is a graph-theoretic structure for representing and solving queries in probabilistic expert systems. The knowledge representation can be viewed from three

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hierarchical levels: topological, functional and numerical. At the topological (or symbolic) level an influence diagram is an acyclic directed network with nodes representing variables critical to the problem and the arcs representing their interrelationships. The nature of these interrelationships is further specified at the functional level. Formal calculi have been developed for deterministic functions and probabilistic relationships (based on either Bayesian or fuzzy probabilities [23], [38]). Finally, the functional form associated with each influence is quantified at the numerical level. 4.1. I N F L U E N C E D I A G R A M S T R U C T U R E

The topological is perhaps the most powerful level of the influence diagram. It is at this level that the structure of complex inference and control problems can be extracted from domain experts. The intuitive graphical representation consists of nodes and directed arcs. The variables can be of three basic types: (1) rectangular control or decision nodes, where the variable is subject to direct control (2) circular state nodes, which correspond to the uncertain quantities that are not directly controllable, and (3) a single diamondshaped value node, in which the utility function is specified for a particular application. The interpretation of the relationships represented by the arcs depends on the type of the nodes they connect. Arcs going into state nodes represent conditional influences as shown in Figs l(a), (b) and (d). The influence can be deterministic, probabilistic or fuzzy. Arcs between state nodes can be reversed through legal topological transformations on the diagram according to Bayes' rule and providing a cycle is not introduced. Arcs to and from control nodes serve a different function and cannot be reversed without changing the basic structure of the model. An influence arc from a control node to a state node (Fig. l(d)) indicates causality in the sense that each control option restricts the event space of the state variable. Arcs going into control nodes are informational and show which variables will be known at the time a decision is made (Fig. l(c)). No-forgetting arcs are placed between control nodes to signify that control decisions are sequential in time and the value of past decisions is remembered. Arcs into the single value node signify which nodes directly influence the goal (Fig. l(f)). The lack of an arc is in some ways a stronger statement of the modeler's knowledge of the system than the existence of an arc. The presence of an arc indicates that a possible dependency exists, while the lack of an arc states strongly that no dependency exists. Consider the two node influence diagram in Fig. 2(a). The state nodes represent the

1

I

Figure 1. Six interpretations of arcs. (a) Probabilistic; (b) fuzzy; (c) informational; (d) causal; (e) noforgetting; (f) value influence.

0

(a)

--OO

(b)

(D

Figure 2. Two state node influence diagrams. (a) x influences y; (b) x is independent of 9.

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variables x and y, and the arc between them indicates that a conditional influence may exist. At a qualitative level, a variable is said to influence a state node y if information about x gives new information about y. At a probabilisfic level the influence diagram in Fig. 2(a) represents the following expansion of the joint probabilities of state variables x and y: Pr (x, y[ H ) = Pr (ylx, H) Pr (xl H ) , where Pr (ylx, H) represents the probability distribution for y conditioned on x and the history or state of information H. The lack o f the arc between the two state nodes in Fig. 2(b) indicates conditional independence; knowing x gives no new information about the state of y. The joint probability in Fig. 2(b) is represented by the expansion: Pr (x, Y l H ) = Pr (Yl H ) Pr (xl H). 4.2. THE D I A G N O S T I C I A N ' S PROBLEM

Consider a system with the set of state variables denoted by X = {x~, x2... x,}. Let S be a set of sensors or observables within the system and F be a set of potential failure nodes, such that X ~ S = {sl s 2 . . . s,,} and X ~=F = {flf2 • • .fj}. The diagnostician's problem is to assess the likelihood of failures for various combinations of hypothesised states given some combination of sensor readings. In mathematical terms the diagnostician's problem [14] can be stated as determining Pr ( F IS, H). The single-valued diagnostician's problem is simply to identify the most likely failure event in the set F given the sensor readings S and the state of information H (i.e. to identify ' T ' such that Pr (f~[S, H)t> ar (f~lS, H) Vi # j ) . 4.2.1. Classification of state nodes For applications in automated manufacturing three categories of state nodes are useful (Fig. 3). Sensor nodes. A sensor node represents sensor measurements that can be directly observed by the operator or controlling system. It might also represent a physical state of the system that is immediately obvious to the operator's senses, such as sight, hearing or smell. Often features from the raw data are used as sensor nodes in the influence diagram model. In our milling machine example the sensor nodes are features from current, dynamometer, and acoustic emission measurements. Failure nodes. As their name implies, these represent states of physical components in the system which may be the cause or symptom of, or contribute to, the initiation of the diagnostic search. Since a sensor itself may fail, some or all of the sensor nodes may belong to this class. Possible failure events for the milling machine failure node are "chatter", "tool wear" and "tool break". Intermediate nodes. These are neither sensor or possible failure nodes, rather they represent nodes which have influences from or to those nodes. They generally represent intangibles in the problem which cannot "fail" or which are not measured directly by any sensor in the system. They are useful in modeling complex mechanical and manufacturing systems and in providing a structure for updating system parameters with human and statistical knowledge obtained over time.

C) {o)

{b)

C) (e}

Figure 3. Types of state nodes. (a) Sensor node; (b) failure node; (c) intermediate node.

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Inference, in response to a specific query to the influence diagram, is performed by transformations on the diagram that reduce it to the topological structure represented by the query. Although many kinds of transformations are possible, the IDES is based on the following two [14]:

Node removal (single arc corollary). A node x with direct predecessors D~ which precedes only one node y can be removed from an influence diagram by absorbing it into the node y. Node y then inherits all the direct predecessors of x. Node absorption is shown schematically in Fig. 4, where D,. represents the set of direct predecessors of y, excluding node x. Note that the sets /9,. and D~ may have node elements in common. The value of an observable sensor node is simply propagated into its successor and no summation is necessary for its absorption. Node absorption corresponds to summation of the joint probability over all possible states of the conditioning node, I2x Pr(ylD~t3D,,) = ~ Pr (ylx, Dr) Pr (x] Dx).

(1)

D,

Arc reversal. An arc from state node x to state node y can be reversed, provided x does not have more than one path to y (so as not to cause a cycle). On reversal of the arc from x to y, node y inherits all the direct predecessors of node x and vice versa (Fig. 5). Arc reversal corresponds to application of Bayes' rule for conditional probability. Example: Sensor-based inference Suppose we know that a failure node F influences an intermediate state variable I that can only be observed through sensor node S (Fig. 6(a)). At a numerical level, this implies that we have Pr (F), Pr (I IF) and Pr (S I I). Sensor-based inference is estimating the likelihood of a failure from a specific value of the sensor reading, i.e. Pr ( F I S). This can be accomplished by sequential application of the rules for arc reversal and node absorption as shown in Fig. 6(b)-(c).

(o)

(bl

Figure 4. (a) Before and (b) after state node removal.

(o)

(b)

Figure 5. (a) Before and (b) after state node arc reversal.

(o)

(b)

(e)

Figure 6. Sensor-based inference with goal: Pr (F[ S). (a) Original model; (b) absorb I into S; (c) reverse arc from S to F.

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Each topological operation in Fig. 6 corresponds to the following functional evaluations that begin with the known quantities {Pr (F), Pr (I I F) and Pr (SI I)} and end with the desired information, Pr (F IS). Fig. 6(b):

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(4)

4.4. THE CONTROLLER'S PROBLEM The diagnostic problem does not stop at probabilistic inference. Once the diagnostician has evaluated the likelihood of certain events, the next step is to decide what course of action to take. The influence diagram can be extended to include diagnostic control and a value node which captures the cost (or utility) of various combinations of events and control decisions. The solution to the controller's problem requires finding the set of decisions (or control instructions) that give the optimal expected cost (or utility) as represented by the diamond-shaped value node. Decision options in the milling machine example represent control over (1) depth of cut, (2) cutting speed, and (3) feedrate. One additional transformation is needed in order to solve the controller's problem.

Control node removal. If a control node directly precedes the value node and if all other conditioning predecessors of the value node are also informational predecessors of the control node, then the control node may be removed by maximising the expected utility of the value node, conditioned on the values of the informational predecessors. The optimal control strategy is assumed for all subsequent operations on the diagram [37]. 4.5. 1DES IMPLEMENTATION 4.5.1. Knowledge representation and acquisition Encoding diagnostic knowledge as influence diagrams has specific advantages over rules in conventional expert systems even when certainty factors can be specified for each rule [16]. In particular, certain classes of dependencies cannot be modeled by rules in a natural or efficient manner unless strong forms of conditional independence are assumed. The IDES implementation allows the expert to explicitly model dependencies in a compact and easily understood graphical representation. Influence diagrams capture not only "heuristic" or "shallow" knowledge but also attempt to encode explicitly the structure of the physical model in the mind of the expert. Further, IDES explicitly allows the possibility of aberrations and failures of sensors themselves. 4.5.2. IDES architecture The IDES architecture is highly modular in form. Constructed on the expert system paradigm requiring separation of control and data, the domain knowledge base is separated from the solution procedure. In addition, the control strategy for the solution is domain-independent. The knowledge base consists of the influence diagram at all three levels of specificity. The control strategy consists of algorithms that create a sequence of query. This separation of the knowledge base from the control strategy provides a flexibility in the architecture that allows the user to change the structure and the parameters of the model in real time.

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IDES

E ........i

Topo

i

.....................

Inform

G

Figure 7. IDES architecture. The I,DES architecture consists of separate modules for knowledge base development, user interaction, control, and co-ordination of the modules (Fig. 7). Inform is the module for developing the influence diagram representation and controls the interaction with the experts or knowledge engineers during model development [39]-[41]. IDES Interface is the module which co-ordinates all of the modules and is the interface with the user [42]. Topo and Numer are the modules for control of the solution at the topological (or symbolic) and numerical levels, respectively. There are several search algorithms employed by Topo, the default "greedy" search algorithm is included in the Appendix of this paper. More details on the theoretical foundation behind the algorithm and a complexity analysis can be found in Rege and Agogino [43], [44]. Simul is a simulation module for testing the efliciency of solution algorithms [40]. In the schematic of the IDES architecture given in Fig. 7 control lines are solid and data lines are dotted. 5. MILLING MACHINE CONFIGURATION In order to illustrate its theory and demonstrate its performance, IDES was applied to a numerically-controlled (NC) upright Bridgeport milling machine. The sensors used were acoustic emission, dynamometer and AC Current as shown schematically in Fig. 8. The amplified sensor outputs were fed into an I N T E L 310 computer based on an I N T E L 80286 microprocessor equipped with an analog to digital ( A / D ) convertor as an add-on board. Data were acquired from four channels, one each for the AE signal, dynamometer X and Y directions, and the current sensor. To sample and store the data from all four channels required approximately 750 0.see. Data were sampled every 1 msec for a variety of cutting parameters and machining conditions. The data were then uploaded from the I N T E L 310 to a DEC Microvax computer where a "real-time simulation" was performed. The simulation reads the actual sensor data from files as if it were reading them in real time. Feature extraction from the multiple sensor data is performed over sets of 100 sensor readings, which corresponds to a time slice of 100 msee in real time. The features extracted are then mapped to the values of sensor nodes in the influence diagram. IDES gives a diagnosis of the current state of the

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180286 microprocessorI Figure 8. Schematic o f the milling machine set up.

machine tool and also recommends a decision to the operator or controller based on these sensor readings. The simulation then continues on to the next 100 msec time slice. 5.1. FEATURE IDENTIFICATION AND EXTRACTION

Feature identification is the first step in the knowledge acquisition process, in which signal features and their correlations with the critical unobservables are encoded into the influence diagram model. Feature extraction in an influence-diagram-based system denotes the mapping of the numerical sensor readings to symbolic values of the sensor nodes. Nii et al. [.45] refer to this as the "signal-to-sensor" problem in building expert systems. In the milling machine example, some of the features extracted require a time history of the sensor output. For example, to extract the frequency content of the acoustic emission signal a Fourier transform of the signal based on its time history is required. Feature extraction in our example was performed over 100 msec time slices. Each of these time ,slices consisted of 100 data points, each sampled every 1 msec. In general the length of the time slice used will be limited by the response time required by the application. In controlling a milling machine a fast response time is desirable. We have found the 100 msec time slice to give satisfactory results; however, on some machines faster responses may be preferable for detecting tool breakage. 5.1.1. Acoustic emission An Endevco 920A piezoelectric transducer mounted directly on the workpiece was used for monitoring acoustic emission. This sensor has a frequency range of 50 kHz to 1MHz. The output from the crystal was fed throt=gh a pre-amplifier, then an amplifier, then a bandpass filter with a range of 50 kHz to 1 MHz, and finally into a hardware rms filter. This rms signal is directed through an A / D converter in the I N T E L 310 computer for data acquisition. The raw A E signal occurs in.bursts at a fret]uency of up to 1 M H z which is too high for sampling by a microcomputer. The rms, on the other hand, can be

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sampled at around 1 msec, which fits well within the 1 msec sampling time of the 1NTEL 310. As discussed in Section 3.5, the rms of the AE signal reflects the amount of energy input to workpiece and thus gives important information on the machine state [31], [46]. Chatter is the self-excited vibration that occurs when a machine tool exceeds its stability limit, which is dependent on the cutting force and the stiffness of the machine. When chatter occurs, the AE signal increases dramatically in amplitude. This can be seen quite clearly by comparing Fig. 9 with Fig. 10. Figure 9 shows the AE rms signal for entry of the cutting tool into the workpiece with no chatter. Figure 10 shows an entry cut with chatter. The large initial amplitude in both cases is due to the entry of the tool into the workpiece. Once the tool is completely in the workpiece this large amplitude dies down when chatter is not present. When chatter is present, however, this high amplitude is maintained. Thus the AE magnitude and change in magnitude are good indicators of chatter. Further the frequency content of the signal can be used to strengthen the hypothesis of whether chatter is present or not. If detected, chatter can usually be corrected by reducing the depth of cut or feed rate. If left undetected, chatter can cause tool, workpiece or machine damage. 4

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In building the influence diagram for the acoustic emission features (Fig. 11), the AE chatter state was indicated by the magnitude of the signal (difference between maximum and minimum values), the change in magnitude from the previous 100 msec time slice, and the frequency content. At the numerical level, the frequency node was represented by binary values: " o n " only if the energy levels of frequencies a b o v e 1 0 0 H z w e r e a b o v e characteristic levels for the machine. Because the average AE signal increases as the tool wears, the mean rms value can be used to detect tool wear. In Fig. 12 " A M E A N " represents the change in the average AE signal from the previous time slice to the present one. The AE signal also provides excellent information concerning tool breakage. The AE rms typically exhibits a high amplitude peak at the moment of tool fracture which is followed by a sharp drop in signal amplitude to a level below that of normal machining. In our influence diagram model for tool breakage, the peak AE rms is compared to a threshold level. The " P E A K " node is a binary node that is set to "yes" if the maximum AE rms exceeds the threshold and " n o " if not. 5.1.2. Force sensing A Kistler 9257A dynamometer (piezoelectric transducer) was used to measure the force in the milling plane of the workpiece. The dynamometer was bolted to the milling machine table and the workpiece bolted onto the dynamometer. The signal was fed to a charge amplifier, converted to a proportional voltage, and sent through an A / D convertor before being sent to the Intel 310 data acquisition computer. When chatter occurs, the amplitude of the dynamometer signal increases and new frequencies are introduced, especially in the direction of chatter (Fig. 13). An increase in tool wear results in an increase in the cutting force. By keeping track of the magnitude and the change in the cutting force rms along both X and Y directions it is possible to observe worsening tool wear. Because the Kistler dynamometer is extremely sensitive to any variation in the force applied to the workpiece, the increases in the forces during entry and exit of the multi-toothed milling tool must be taken into accounL As shown in the influence diagram in Fig. 13, the magnitude and change in rms of the dynamometer signal are used to detect the wear state, in conjunction with the use of the rms to determine

Figure 11. Influence between acoustic emission features and chatter state.

Ca)

(b)

Figure 12. Acoustic emission features that influence (a) wear and (b) tool breakage.

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whether the cut (shown as the intermediate node labeled " C U T D Y N " ) is in an entry or exit state. The d y n a m o m e t e r can also be effective for detecting tool breakage. This is accomplished by detecting a pattern in the signal showing a large rise followed by a drop and finally a continued value at a level above the previous average value. These " b r e a k " patterns are represented in the d y n a m o m e t e r portion of the influence diagram by the nodes labeled " B R E A K X" and " B R E A K Y" in Fig. 13. This pattern recognition technique for the IDES implementation has proven to be both effective and fast, requiring much less signal processing than techniques using time series analysis and linear discriminant functions. 5.1.3. M o t o r current An American Aerospace Controls series 1003AM1 AC current sensor was used to measure spindle motor current. This is an induction sensor that requires no interruption of the input power line. The AC signal induced in the sensing element is rectified and filtered giving a DC voltage output that is directly proportional to the AC line current. Because the signal processing techniques used for IDES are largely based on the pattern of the signal and not on the absolute magnitude, only one phase of the three-phase motor current was monitored.

Figure 13. Features of the dynamometer signal.

2"4 2.35

-

~

2"3 2"25 E

2-2 u

"6 2.15 2-I 2-05 2

0

0.2

0-4

0.6

0"8 I 1.2 Time (sec)

1.4

1"6-

1.8

Figure 14. Motor current vs. time for entry cut without chatter.

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The current drawn by the motor is directly proportional to the torque exerted by the motor and hence is proportional to the cutting force. Because of the sharp changes in the motor load during the tool entry and exit, the current sensor is an excellent detector of the cut state. This is extremely valuable because these transitions are difficult to distinguish by the other sensors. For example, the AE sensor exhibits a large amplitude both during entry of the tool into the workpiece and during chatter. Figure 14 shows the rise in spindle motor current and leveling off as the tool enters the work. Figure 15 shows the current dropping off to the zero load value as the tool leaves the workpiece. 5.2. SENSOR FUSION By combining the influence diagram modules in Figs. 11-13 and adding the effect of the current sensor node we get the overall influence diagram of the system shown in Fig. 16. The object of the diagnostician's problem is to determine the machine state given the observable sensor readings. The single failure node in Fig. 16 represents the fusion of information concerning the machine state from the qualitative features of the AE dynamometer and current sensors. This node can take on values corresponding to " C H A T T E R " , " W O R N TOOL", " B R O K E N T O O L " and " O K " , which denotes a normal machine state. After solving the influence diagram for the diagnostician's problem, IDES displays the probability distribution of the query node " M A C H I N E STATE", i.e. the probabilities of each of the node events conditioned on the sensor node readings over the last 100 msec time slice, which have been set just before IDES is called. The IDES inference engine uses Bayesian probability to propagate the implications of the sensor readings to the likelihood estimates for each possible state of the milling machine. Conditional independence is only assumed where there are no arcs drawn in the influence diagram in accordance with both expert opinion and the experimental data. Arcs between sensor nodes are not drawn under the assumption that all diagnostic queries will be conditioned on these nodes. If a decision is to be transmitted to the operator or machine controller, IDES can be used to solve the controller's problem. The possible decision options represented by the single control node in Fig. 16 are: "reduce depth of cut", "retract tool" or "no change". IDES uses a dynamic programming algorithm to optimise a parameterised utility function over the control options and assessed uncertainty of the possible failure states. 2"4 22"5~ 2" 2"2

o=

2" 2"I,

~g

2' 2"0:

1"9 0

0.2

0.4

0.6

0.8

I

I-2

I-4

I.,6

1.8

Time (sec) F i g u r e 15. M o t o r c u r r e n t vs. t i m e f o r exit c u t w i t h o u t c h a t t e r .

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Features of AE signal

~

Controller's

Qualitative states i

'

]

Diagnostician's

problem

Features of dynamometer siqnal

Sensor fusion Qualitative states

Figure 16. Influence diagram for milling machine diagnostics, monitoring and control.

The results are presented graphically for each 100 msec time slice (Fig. 17). Under the plot of the signal reading for each sensor are the magnitudes of the two most dominant frequencies and their relative energy levels (normalised to one). The probability distribution of the machine state and the recommended control decisions are highlighted below the signal plots.

6. DEVELOPMENT AND TUNING The topology of the influence diagram was obtained by encoding knowledge about the relevant features extracted from the raw sensor readings. This knowledge was obtained by a dialogue with experts and a study of literature in the area. The probability distributions encoded at the numerical level of this model were based on heuristic estimates that were tuned for the specific machine tested. Topological transformations on the influence diagram were performed in order to obtain a structure that requires the fewest number of operations in answering the expected diagnostic and control queries. To assess the relative importance of the sensors, the system was modified to exclude the d y n a m o m e t e r sensor. The dynamometer sensor is intrusive and is not very practical for use in most day-to-day machining operations. It was found that reliable predictions could be made even after excluding the dynamometer, though as expected, the uncertainty factor was wider than when the d y n a m o m e t e r was included.

MULTIPLE SENSOR EXPERT SYSTEM

AE S i g n a l Mog : 1274 Freq RPL 50 0.7902 60 I" 0 0 0 0

Dynamometer-x Mog: 1042 Freq RPL 60 0.879:5 II0

Machine state

( ~

1.0000

Probability

Broken

0.00

High wear

0-00

Chatter

0.8,5

OK

0.15

Dynomometer-y Mag: 1620 Freq RPL 50 0.8627 60 1-0000

181

Motor current Mag: 016 Freq RPL I10 1-0000

Decisions • Depth of cut

Decrease

• Feed rate

Same

• Cutting speed

Same

Figure 17. 1DES display for milling machine diagnostics, monitoring and control. Freq, frequency; RPL, relative power level. 7. RESULTS I n twenty-two trial cuts the I D E S system m a d e correct d i a g n o s t i c predictions in sixteen cases a n d o p t i m a l control decisions in n i n e t e e n cases. The event with the m a x i m u m p r o b a b i l i t y was c o m p a r e d to the state actually observed in the cut in each of these cases. I n the n i n e t e e n o p t i m a l control cases the decision of the system c o r r e s p o n d e d to that TABLE 1

Detection of chatter with control decision to "reduce depth of cut" Probability With dynamometer Without dynamometer

OK

CHATTER

WORN

BROKEN

0.15

0.85

0-00

0.00

0.00

0.47

0.38

0.15

Decision Reduce depth of cut Reduce depth of cut

Trial cut number: D4D, depth of cut, 60 1/1000 in; feed rate, 15 in/min; cutting speed, 1600 rpm; samples, 3000; description, end exit/chatter; time slice no. 2. TABLE 2

Ambiguous inference and conservative decision to "'reduce depth of cut" Probability

OK

CHATTER

WORN

BROKEN

Decision

With dynamometer

0.47

0.53

0.00

0.00

Reduce depth of cut

Trial cut number: D3D, depth of cut, 30 1/1000 in.; feed rate, 15 inches/min; cutting speed, 1600 rpm; samples, 3000; description, normal; time slice no., 3.

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which a human operator of the machine would recommend. The results for a typical cut with and without the dynamometer sensor are shown in Table 1. In the three examples where the decision was suboptimal the error was on the "'safe" side in all cases. Table 2 demonstrates a typical example. The system predicts nearly equal probabilities for " C H A T T E R " and the " O K " state resulting in an ambiguous inference. IDES responds in a robust fashion and does not act on the most likely event, recognising that the data are noisy and the uncertainty level is high. The decision made is to reduce the depth of cut because of the strong possibility that the tool is chattering. The actual state was " O K " (no chatter, tool breakage or worn tool). The ideal decision should have been to make no change in the machine settings. Thus the actual decision recommended is conservative. This reflects the risk aversion in the utility function used in this example. 8. FUTURE RESEARCH The signal processing for this application was performed entirely in software taking approximately 100 msec on a DEC Microvax II. Solution of the diagnostician's problem or controller's problem takes another 100 msec each. Because it is desirable to have much smaller cycle times when implementing the system in a feedback control loop to the machine tool, several hardware options are under investigation: (1) implementing part of the system on a microchip, (2) using add-on boards for signal processing in hardware and (3) using array processing for the numerical computation. The application of IDES to the milling machine monitoring and control problem has stimulated research in the following areas: (1) automatic generation of the influence diagram by inductive learning from a given set of examples and (2) development of techniques for obtaining an optimum mapping from the feature value to the sensor node's symbolic value, and (3) integrating the influence diagram model with neural network schemes. 9. SUMMARY This research demonstrates the feasibility of using expert systems technology for in-process diagnostics, monitoring and control in unattended manufacturing. The IDES methodology integrates heuristic knowledge concerning the interpretation of sensory data, first-principle knowledge concerning material behaviour and numerical data obtained by experimentation. The fusion of information from multiple sensors exploits the strengths of each sensor while minimising its drawbacks with relatively simple signal processing. The non-determinism of the mapping between "noisy" sensor data and machine state is addressed explicitly and in a theoretically rigorous manner by means of Bayesian probability. Dynamic programming is used to optimise the control decisions over the cost function for each machine under the inferred uncertainty of possible failure states. The parameterised cost function can be adjusted to take into account the cost trade-otis and risk aversion of any specific machining operation. ACKNOWLEDGEMENTS The authors would like to thank Dave A. Dornfeld and Sabbir S. Rangwala of the RAMP (Robotics, Automation, Manufacturing and Production) Group in the Department of Mechanical Engineering at the University of California, Berkeley for their expert advice and assistance in developing the milling machine application. The authors also

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acknowledge Ashutosh Rege for his theoretical contributions concerning sensor-based inference using influence diagrams. This work was f u n d e d in part t h r o u g h N S F grant DMC-8451622 and Project M I C R O and General Electric's G r a n t 85-165.

REFERENCES 1. E. H. SHORTL1FFE and B. G. BUCHANAN 1975 Mathematical Biosciences 23, 351-379. A model of inexact reasoning in medicine. 2. H. POPLE 1977 Proceedings of the 5th International Joint Conference on Artificial Intelligence, AAAI 1030-1037. The formation of composite hypotheses in diagnostic problem solving: An exercise in synthetic reasoning. 3. J. S. A1KINS, J. C. KUNZ, E. H. SHORTLIFFE and R. J. FALLAT 1983 Computers and Biomedical Research 16, 199-208. PUFF: An expert system for interpretation of pulmonary function data. 4. S. S. WANG, S.-Y. LEE, W. IMA1NO, L. CRAWFORTH and A. JULIANA 1987 Proceedings of the 1987 ASME International Computers in Engineering Conference 1, 221-227. Knowledgebased acoustic pattern prediction for copier diagnostics. 5. D. L. LARNER 1987 Proceedings of the 1987 ASME International Computers in Engineering Conference 1, 203-207. NC-CONSULTANT: An expert system for NC machine diagnosis. 6. S. C. KUE! and T. E. BURTON 1987 Proceedings of the 1987 International ASME Computers in Engineering Conference 1, 73-78. Polyfill: A flow simulation code for injection moulding. 7. R. H. PHILLIPS, V. ARUNTHAVANANATHAN and S. D. ZHOU 1986 Knowledge-based Systems for Manufacturing, ASME-PED 24, 263-273. MICROPLAN: A microcomputer based expert system for generative process planning. 8. P, K. FINK 1985 Proceedings of the 9th International Joint Conference on Artificial Intelligence, A A A I 1, 426-431. Control and integration of diverse knowledge in diagnostic expert systems. 9. D FREED and D. WRlGHT 1984 Proceedings of the 1984 International ASME Computers in Engineering Conference and Exhibit 2, 338-342. FAXS: An expert system for the analysis of mechanical failures. 10. J. S. BENNETT and C. R. HOLLANDER 1981 Proceedings of the 7th International Joint Conference on Artificial Intelligence, A A A I 2, 843-845. DART: An expert system for computer fault diagnosis. 11. P. P. BONISSONE and H. E. JOHNSON Jr. 1984 Human Systems Management 4, 255-262. Expert system for diesel locomotive repair. 12. P. K. WRIGHT and D. A. BOURNE 1988 Manufacturing Intelligence. Reading, MA: AddisonWesley. 13. A. M. AGOGINO 1985 Proceedings of the 1985 International ASME Computers in Engineering Conference 2, 305-310. Use of probabilistic inference in diagnostic expert systems. 14. A. REGE and A. M. AGOG1NO 1986 Knowledge-based Expert Systems for Manufacturing. ASME-PED 24, 67-83. Sensor-integrated expert system for manufacturing and process diagnostics. 15. M. S. FOX 1983 Proceedings of the 8th International Joint Conference on Artificial Intelligence, A A A I 1, 158-163. Techniques for sensor-based diagnosis. 16. D. E. HECKERMAN and E. J. HORVITZ 1987 Proceedings of the 6th National Conference on Artificial Intelligence, A A A I 1, 121-126. On the expressiveness of rule-based systems for reasoning with uncertainty. 17. P. CHEESEMAN 1985 Proceedings of the 9th International Joint Conference on Artificial Intelligence, A A A I 2, 1002-1009. In defense of probability. 18. S. HOLTZMAN 1985 Ph.D. Dissertation, Engineering-Economic Systems, Stanford University. Reprinted by Strategic Decisions Group, Menlo Park, CA. Intelligent decision systems. 19. J. PEARL 1986 Proceedings of the 5th National Conference on Artificial Intelligence, A A A I I, 339-343. On the logic of probabilistic dependencies. 20. M. HENRION 1987 Proceedings of the 3rd Workshop on Uncertainty in Artificial Intelligence, A A A I 132-139. Practical issues in constructing a Bayes's belief network. 21. E.J. HORVITZ, D. E. HECKERMAN and C. P. LANGLOTZ 1986 Proceedings of the 5th National Conference on Artificial Intelligence, AAAI 1,210-214. A framework for comparing alternative formalisms for plausible reasoning.

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22. M. HENRION and D. R. COOLEY 1987 Proceedings of the 6th National Conference on Artificial Intelligence, A A A I 2, 471-476. An experimental comparison of knowledge engineering for expert systems and for decision analysis. 23. A. M. AGOG1NO and A. REGE 1987 Mathematical Modelling 8, 22?-233. IDES: Influence diagram based expert system. 24. J. TLUSTY and G. C. ANDREWS 1983 Annals of the CIRP 32, 563-572. A critical review of sensors for unmanned machining. 25. J. L. STEIN and K.-C. SHIN 1985 Sensors and Controls .for Automated Manufacturing and Robotics, ASME-PED 18, 57-66. Current monitoring of field controlled DC spindle drives. 26. J. L. STEIN, D. COLVIN,G. CLEVER and C.-H. WANG 1984 Sensors and Controls for Automated Manufacturing and Robotics, ASME-PED 18, 45-63. Current monitoring on DC servo machine tool feed drives. 27. V. ALTINTAS, I. YELLOWSLEY and J. TLUSTY 1985 Sensors and Controls in Manufacturing, ASME-PED 18, 41-48. The detection of tool breakage in milling. (Note: The name of Y. Altintas was mis-spelled as Y. Attanis in the publication.) 28. M.-S. LAN and Y. NAERHEIM 1985 Sensors and Controls in Manufacturing, ASME-PED 18, 49-56. In process detection of tool breakage in milling. 29. S. BRAUN, J. ROTBERG and E. LENZ 1987 Mechanical Systems and Signal Processing l, 185-196. Signal processing for single tooth milling monitoring. 30. E. N. DIE1 and D. A. DORNFELD 1985 Sensors and Controls in Manufacturing, ASME-PED 18, 75-84. Acoustic emission from the face milling process--the effect of process variables. 31. D. A. DORNFELD 1985 Proceedings 12th N S F Conference on Production Research and Technology, SME. Acoustic emission monitoring and analysis of manufacturing processes. 32. E. N. DIE! and D. A. DORNFELD 1985 Sensors and Controls in Manufacturing, ASME-PED 18, 33-39. A model of tool fracture generated acoustic emission during machining. 33. A. C. MILLER, M. M. MERKHOFER, R. A. HOWARD, J. E. MATHESON and T. R. RICE 1976 Final Technical Report, D A R P A # 2742, SRI International, Menlo Park, CA. Development of automated aids for decision analysis. 34. R. A. HOWARD and J. E. MATHESON 1984 The Principles and Applications of Decision Analysis 2, Strategic Decisions Group, Menlo Park, CA. Influence diagrams. 35. S. M. OLMSTED 1984 Ph.D. Dissertation, Engineering-EconomicSystems Department, Stanford University. On representing and solving decision problems. 36. R. D. SHACHTER 1985 Proceedings of the A A A I Workshop on Uncertainty and Probability in Artificial Intelligence, 237-244. Intelligent probabilistic inference. 37. R. D. SHACHTER 1986 Operations Research 34, 871-882. Evaluating influence diagrams. 38. P. JA1N and A. M. AGOGINO 1987 Working Paper No. 87-0803-3 Expert Systems Laboratory, Department of Mechanical Engineering, University of California, Berkeley. Arithmetic operations on fuzzy probabilities. 39. E. A. MOORE and A. M. AGOGINO 1987 International Journal of Man-Machine Studies 26, 213-230. INFORM: An architecture for expert-directed knowledge acquisition. 40. M. BRONSTEIN 1987 Expert Systems Laboratory, Dept. of Mechanical Engineering, University of California, Berkeley. Documentation for IDES: Influence diagram based expert system. 41. M. LAMBERT and A. M. AGOGINO 1987 Proceedings of the 2nd International Joint Conference on Human-Computer Interaction, 324. A graphical interface to an influence diagram based expert system. 42. K. RAMAMURTHI 1988 User's Manual for GraphlDES: Graphical influence diagram based expert system. Expert Systems Laboratory, Dept. of Mechanical Engineering, University of California, Berkeley. 43. A. REGE and A. AGOGINO 1986 Proceedings of the ICS-86. International Computer Symposium, IEEE 3, 1685-1691. Representing and solving probabilistic inference problems in expert systems. 44. A. REGE and A. M. AGOGINO 1988 IEEE Transactions on Systems, Man and Cybernetics. Topological framework for representing and solving probabilistic inference problems in expert systems (to be published). 45. H. P. Nil, E. A. FEIGENBAUM, J. J. ANTON and A. J. ROCKMORE 1982 Spring A I Magazine, 23-35. Signal-to-symbol transformation: HASP/SIAP case study. 46. D. A. DORNFELD 1984 Proceedings o f the Symposium on Sensor Technology on Untended Manufacturing, SME, The role of acoustic emission in manufacturing process monitoring.

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APPENDIX A iDES "'GREEDY"

TOPOLOGICAL

SEARCH

ALGORITHM

FOR PROBABILISTIC

INFERENCE

[44]

Input An influence diagram G = (V, A), the goal node xi and the conditioning nodes xj;

i~J Output An influence diagram G ' consistent with G representing Pr (xilxj). Let J ' = {i} w J, nodes to be retained; K ' = {1, 2 , . . . , n}/J', nodes to be removed. Let K be the "current" version of K'. Initially K -- K'. 1. Barren node removal. Remove all nodes in K which have no successors--update the successor sets for nodes which directly preceded the barren nodes. If K is empty go to step 4. 2. State node removal. Remove all nodes in K which have only one successor each. The removal is done in order of number of predecessors--with the node having the least number of predecessors removed first. Update successors, predecessors etc. Repeat till all nodes in K with only 1 successor are exhausted. I f K is empty go to step 4 else go to step 3. 3. Arc reversal. Select the node in K with the least number of successors (i.e. least out-degree). Let this node be x. * Pick a successor of x, check if there are more than one paths from x to that successor. If yes then reject it, else say y is that successor. Reverse the arc (x, y) with concomitant updating of predecessor, successor sets etc. Check if x has only one successor--if so remove x and if K is not empty go to step 2. If K is empty go to step 4. Otherwise go back to * 4. Arc reversal from goal one paths from xi to that successor which has the concomitant updating of successors.

node. For every successor of xi, check if there are more than successor. If yes then reject that successor, else keep track of least number of predecessors (say y). Reverse ( x , y) with successor and predecessor sets. Repeat step 4 till x~ has no