A pattern recognition based vibration monitoring module for machine tools

RoboUcs & Computer-Integrated Manufacturing, Vol 4, No 3/4, pp 465--469, 1988

0736-5845/8853 00 + 0 00 © 1988 Pergamon Press plc

Pnnted in Great Britain

• Paper

A PATTERN RECOGNITION BASED VIBRATION MONITORING MODULE FOR MACHINE TOOLS P. BARTAL and L. MONOSTORI Computer and Automation Institute, Hungarian Academy of Sciences, Kende u. 13-17, Budapest, H-1502 Hungary The unmanned operation of manufacturing cells and systems requires automated monitoring equipment, as a substitute for human beings in their supervisory capacity. Concentrating on the frequency domain analysis of measured signals, this paper presents a short summary of the available spectral and cepstral features applicable for describing the different states of the machine or the process to be monitored. Pattern recognition techniques are suggested for the classification phase of the monitoring process. Regarding the requirements of a vibration monitoring module, integrated into a complex, multipurpose machine tool monitoring system, a comparison is made between the different computational devices in performance, complexity and intelligence. The hardware and software architecture of the two-processor based module--incorporating a DSP and a 16 bit microprocessor in parallel architecturenis discussed.

1. INTRODUCTION The fast evolution of computational devices, along with the rapidly increasing complexity of manufacturing systems, has resulted in new perspectives towards operating these systems with reduced human supervision. The replacement of human beings in their monitoring functions, by modular multipurpose machine tool monitoring systems, is one of the newest trends in the world's machine industry. 6 Since different state-changes of the machine tool and the manufacturing process exert a significant influence on the vibration of participating mechanical components, the frequency spectrum provides information (usually in distributed form) concerning the actual state of the manufacturing process. Surprisingly, only a few on-line vibration monitoring instruments exist, due to the absence of low-cost powerful digital ICs and the difficulties involved in interpreting the calculated spectra. The appearance of new high-capacity VLSI ICs focuses attention on the development of special purpose vibration monitoring equipment together with more sophisticated classifying methods.

This paper presents a short summary of the existing digital methods for obtaining spectral parameters and the most commonly used decision algorithms. Attention is drawn to the pattern recognition approach in the feature selection and classification phases. In order to find an optimal solution various kinds of computational devices are compared with respect to speed, complexity and flexibility. The chosen hardware and software structures of the vibration monitoring module are described; these constitute an integrated part of a multipurpose machine tool monitoring system being developed in the Computer and Automation Institute, Budapest, Hungary. 8

Acknowledgements--A part of this investigation was supported by the State Office for Technical Development and the VILATI CNC manufacturing company. Special

thanks are due to Mr. Ott6 B~inhegyl and his colleagues for their help and cooperaUon.

2. DIGITAL METHODS, APPLICABLE FOR OBTAINING SPECTRAL FEATURES The process of vibration monitoring (Fig. 1) can be regarded as the concatenation of four distinct operational phases, which represent the consecutive steps of a pattern recognition model of monitoring. 7 Selected analog input signals are sampled and converted into digital form during the data acquisition phase.

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Robotics and Computer-Integrated Manufacturing • Volume 4, Number 3/4, 1988

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The differences between various measurements can be better observed in the frequency domain: a transformation is performed in the preprocessing phase. Computation of several spectral and cepstral features from the spectrum allow the possibility of decision-making on the basis of a reduced set of data using elementary or more complex monitoring algorithms. The most commonly used method for determining the frequency spectrum of sampled signals is the Fast Fourier Transform. 3 The computing requirement of the procedure amounts to N . 2log(N) complex multiplications, where N represents the number of samples. In time critical applications, either spectrum estimation 1° or Walsh Transforms 2'5 could be more advantageous because, instead of complex multiplications, only real additions and subtractions are needed. By combining the Walsh coefficients in a way similar to the combination of the Fourier ones, the Walsh power spectrum can be calculated. If only certain ranges of the Fourier spectrum are to be determined, computation of the coefficients within these ranges can be carried out more efficiently from the Walsh spectrum. In machine tool monitoring, the significance of cepstral methods, in addition to spectral ones, has become known recently. 13 The cepstrum of the time function x(t) is defined as follows:

rency ranges can be mentioned as useful parameters. It will be shown later that the new family of high capacity VLSI ICs comply with the real-time requirements, and on-line machine tool vibration monitoring modules can be based on the Fast Fourier Transform.

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where q denotes quefrency, a time dimensional quantity analogous to the frequency, while F corresponds to the Fourier Transform. Since the spectrum/cepstrum consists of a great number of lines, process classification is made on the basis of one or more spectral/cepstral features which appropriately characterize the spectrum/cepstrum. Among spectral features the • total power of the signal, • power in a frequency range, • frequency of the maximum peak, • amplitude of the maximum peak, and • normalized spectral moments turned out to be the most important. 4'7'H From the cepstral features, the quefrency of the maximum peak and the sum of cepstrum lines in given quef-

3. SPECTRUM CLASSIFICATION BASED ON PATTERN RECOGNITION Almost without exception, available machine tool monitoring equipment make process classification on the basis of one process parameter (actual value or running average of the measured quantity, its power content in predetermined frequency intervals, etc.) or by the logical combination of such elementary decisions (e.g. if A > A~,m or B > Bhm then ...).7 This logical scheme is a simplification of the general case, when the process to be supervised can be characterized only by the joint analysis of several parameters of one or more measured signals. Machines with malfunctioning elements, or process variables undergoing important changes of state, produce dynamic signals which contain information visible in the frequency domain. The frequency spectrum of the cutting force or the mechanical vibration measured on the workpiece holder or some other part of the machine frame involves information in a distributed form. Its supervision can be considered typical of complex, multiparameter

In the pattern recognition model of vibration monitoring, a pattern vector is formed from the selected spectral and cepstral features and fed into a pattern classifier, which classifies the spectrum during the decision phase. Figure 2 shows the structure of a discriminant function based pattern classifier. If we have R classes, which the patterns fall into, the classifier employs R discriminators, each of which computes the value of a discriminant function. In classifying an n-dimensional pattern x, the R outputs of the discriminators--called discriminants--are compared by a maximum selector, which indicates the largest discriminant and classifies the pattern into the class to which the discriminant function belongs. 9 It should be noted that two class problems (R = 2) can be reduced to one discriminant function

Pattern recogmtlon based vibration momtorlng • P. BARTAL and L. MONOSTORI

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and--instead of the maximum selector--to a threshold element, which evaluates the sign of this single discriminant function. The key aspect of this method is to find the most suitable transformations, useful spectral and cepstral features and the most adequate decision algorithms, by which ttie user is confronted, In order to answer these questions an interactive off-line system has been developed 7 containing pattern recognition methods with relating learning techniques. 4. HARDWARE AND SOFTWARE STRUCTURES OF THE VIBRATION MONITORING MODULE As an integrated part of the complex, modular machine tool monitoring system, the vibration monitoring module must comply with the related real-time and system criteria, 1 as listed below: • monitoring three 1 kHz signals simultaneously or one 5 kHz signal continuously, • configuring the monitoring process in terms of using different transform and decision algorithms, • communicating with the control module through the system bus. Two important consequences of these requirements must be mentioned. On one hand, the module should sample a 5 kHz signal every 100/~s, meaning that it should execute a 1024-point FFT within 100 ms. On the other hand, it should allow on-line programming of various transform and decision

algorithms. Aiming at the development of the hardware architecture of the module, a comparison between computing devices--based on different princ i p l e s - h a s been made with respect to speed, programmability and complexity, resulting in the following. The execution of a 1024-point FFT running on a PDP 11/40 took about 2 s; while using an 8 MHz MC 68000 based microcomputer, it dropped to 700 ms. 13 Further reduction of FFT execution time

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was achieved by speeding up the'multiplications. Testing an Intel 8086 with a 100 ns multiplier/ accumulator resulted in a 250 ms value. Taking more powerful digital ICs into consideration, bit-slice processors have been compared to new digital signal processors (DSP). Although bit-slice devices are expected to meet the criteria, because of their clumsy design they are surpassed by widely spreading DSPs. The new family of DSPs support autonomous operation through their own address, data and control buses which enable them to handle relative large off-chip program and data RAMs. The Harvard architecture of these processors and integration of RAM and hardware multiplier on-chip ensures fast program execution (e.g. the TMS 32010 performs the 1024-point FFT within 80 ms). The powerful instruction sets, containing both dedicated DSP instructions and general purpose instructions, make programming very convenient. 4.1. Hardware architecture Taking all requirements into consideration, the hardware architecture of the vibration monitoring module has been developed as shown in Fig. 3. The module consists of four functional blocks, two of which--namely the HOST and the DSP--contain an intelligent processor which performs the number-crunching calculations, while the remaining two blocks--the global R A M and the data acquisition module--have special tasks. The H O S T block incorporates a 16-bit general purpose microprocessor, the H O S T CPU; this unit has its own EPROM and RAM, each with 16 kbytes, and is appointed to the master of the board. In addition to performing several computational tasks, the HOST CPU communicates with the control module of the complex monitoring system and coordinates the operation of a powerful digital signal processor as well. Having control over its 32 kbyte high-speed DSP R A M , the digital signal processor (DSP) executes dedicated tasks at full speed,parallel with the operation of the HOST CPU. Thanks to the dual-ported implementation of the DSP, RAM programs and initial data can be downloaded during the startup procedure as the entire memory resides in the address space of the HOST CPU. Communication between the two processors is realized through a 4 kbyte global R A M which can be assigned either to the HOST CPU or to the DSP, as needed. The state of this dual-port RAM block is indicated by a hardware semaphore, which can be tested and set by either processor.

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8 analog signals, one of which is fed into a 12-bit A/Dconverter. Channel selection, together with data conversion, is initiated by the HOST CPU while output data are stored by the DSP in the DSP RAM. As conversion is completed, an interrupt is sent to the DSP which stores the digitized data and also interrupts the HOST CPU, indicating completion of the current AID conversion cycle. 4.2. Software structure As a consequence of incorporating the two different processors, the module has a two-level software structure. For full exploitation of the parallel hardware architecture, different tasks are distributed between the processors. With the exception of transformation, all tasks are accomplished by the HOST CPU. While performing communication tasks, the HOST CPU either communicates with the control module of the monitoring system, initiates data acquisition or cooperates with the DSP. Computation of the selected spectral/cepstral features and evaluation of the operative decision algorithm belong to the calculation tasks. Transformation from time into frequency domain is performed by the number crunching DSP. During initialization, the control modules sends all

information (selected input channels with corresponding sampling frequencies, desired transformation, chosen spectral/cepstral features and applied decision strategy) needed to configure each phase of the vibration monitoring process. The HOST CPU stores this information in its local memory, in the form of tables and it is taken as the basis of further monitoring activities. However, information concerning the operation of the DSP is also stored in the global RAM, through which the two processors can be synchronized to each other. As the global memory is simultaneously accessed by the HOST CPU and the DSP, the HOST CPU has full control over the activity of the board; therefore, configuration or modification of the whole monitoring process can be simply done by manipulating the appropriate tables. Arranging the two processors in parallel takes advantage of organizing the individual steps of the monitoring process in a pipelined manner as illustrated in Fig. 4. While acquiring 1024 sampled data, the DSP transforms the previous input data array. Meanwhile, the HOST CPU computes the selected features, processes the former spectrum, and makes a decision. The pace of the pipeline is governed by the actual sampling rate which amounts to 100 ms, in the case of monitoring 5 kHz signals.

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5. RESULTS AND CONCLUSIONS The vibration monitoring module, described above, is an integrated part of the already mentioned modular machine tool monitoring system8 being developed by the Computer and Automation Institute (Budapest) in cooperation with the VILATI CNC manufacturing company. During the design and the development phase of the project special emphasis was placed upon tailoring the concept of the module to VILATI standards, in order to make industrial introduction of the system easier. For the time being, the module operates in a laboratory environment, where several tests have been made concerning throughput and reliability. Initial experiments for monitoring small diameter drills have shown promising results. However. the result of putting the whole monitoring system into serial production will be that software support for the module will have to be continuously produced. Having summarized the existing digital methods for obtaining spectral and cepstral features, pattern recognition based classification of spectra was described. A comparison was made between digital ICs based on different principles regarding throughput and complexity. It was found that the new family of DSPs can advantageously be used to perform transformations involved in the vibration monitoring process. The described hardware and software structure of the module permits configuration of vibration monitoring tasks in many different ways. Containing a 16-bit general purpose microprocessor and a DSP chip in parallel architecture, a speed improvement o f about one order o f magnitude was achieved. Adding frequency domain analysis to the existing preproces-

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sing methods allows more information to be taken into consideration. Easy implementation of other digital methods for computing the power spectra, the selection of various spectral features and several decision strategies (including pattern recognition methods) are effected simply by programming the device. However, thanks to the universal structure of the module, not only frequency domain analysis but also other number-crunching problems can be solved. REFERENCES 1. Bartal, P.: Frequency domain vibraUon monitoring module integrated in real-time machine tool monitoring systems. Proceedings of the 4th IMEKO International Symposium on Technical Diagnostics, Dubrovnik, Yugoslavia, 13-15 October 1986. 3.27-3.30. 2. Beauchamp, K.G.: Walsh Functions and Their Applications. London, Academic Press, 1975. 3. Brigham, E.O.: The Fast Fourier Transform. New Jersey, Prentice-Hall 1974. 4. Dornfeld, W.H., BoUinger, J.G.: On-line frequency domain detection of production machinery malfunctions. Proceedings of the 18th International Machine Tool Design and Research Conference. London, 1977. pp. 837-844. 5. Hermann, Gy., Horv~ith,L., Monostori, L.: Real-time monitoring of machine tools via the WalshHadamard transform. IEEE International Conference on Acoustics, Speech and Signal Processing, Paris, France, 3-5 May, 1982. pp. 343-346. 6. Kegg, R.L.: On-line machine and process diagnostics. CIRP 33: 469-473, 1984. 7. Monostori, L.: Learning procedures m machine tool monitoring. Cornput. Ind. 7: 53-64, 1986. 8. Monostori, L.: Multipurpose machine tool monitoring systems. Proceedings of the 4th IMEKO International Symposmm on Techmcal Dtagnostics, Dubrovnik, Yugoslavia, 13-15 October, 1986. 3.42-3.45. 9. Nilsson, N.J.: Learning Machines. New York, McGraw-Hill, 1965. 10. Sakamoto, Y., Sakai, T., Abe, T., Kohzaki, T.Development of diagnostic technique for rotating machinery by means of vibration measurement. In: Separatum-IMEKO 9th World Congress, Berlin (West), Preprint, 1982. Vol. V, pp. 279-284. 11. Sata, T., Matsushima, K., Nagakura, T., Kono, E.: Learning and recognition of the cuttang states by the spectrum analysis. Ann. CIRP 22/1: 41-42, 1973. 12. Weck, M., Monostori, L., Kiihne, L.: Universelles System zur Prozess- und Anlagentiberwachung, Vortrag und Berichtsband der VDI/VDE-VMR Tagung "Verfahren und Systeme zur technischen Fehlerdiagnose", Langen, F.R.G, 1984. pp. 139-154. 13. Weck, M., Mehles, H.: Uberwachung von Fertigungseinrichtungen und Prozessen. Industrie Anzeiger, Nr. 39., 105 Jg., 18.5.