Development of a turbojet engine gearbox test rig for prognostics and health management

Mechanical Systems and Signal Processing 33 (2012) 299–311

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Development of a turbojet engine gearbox test rig for prognostics and health management Aida Rezaei a, Azzedine Dadouche b,n a b

Queen’s University, Mechanical and Material Engineering Department, Kingston, ON, Canada K7L 3N6 National Research Council, Aerospace Portfolio, Ottawa, ON, Canada K1A 0R6

a r t i c l e i n f o

abstract

Article history: Received 14 December 2011 Received in revised form 27 April 2012 Accepted 30 May 2012 Available online 11 July 2012

Aircraft engine gearboxes represent one of the many critical systems/elements that require special attention for longer and safer operation. Reactive maintenance strategies are unsuitable as they usually imply higher repair costs when compared to condition based maintenance. This paper discusses the main prognostics and health management (PHM) approaches, describes a newly designed gearbox experimental facility and analyses preliminary data for gear prognosis. The test rig is designed to provide full capabilities of performing controlled experiments suitable for developing a reliable diagnostic and prognostic system. The rig is based on the accessory gearbox of the GE J85 turbojet engine, which has been slightly modified and reconfigured to replicate real operating conditions such as speeds and loads. Defect to failure tests (DTFT) have been run to evaluate the performance of the rig as well as to assess prognostic metrics extracted from sensors installed on the gearbox casing (vibration and acoustic). The paper also details the main components of the rig and describes the various challenges encountered. Successful DTFT results were obtained during an idle engine performance test and prognostic metrics associated with the sensor suite were evaluated and discussed. Crown Copyright & 2012 Published by Elsevier Ltd. All rights reserved.

Keywords: Accessory gearbox Defect to failure tests Signal processing Prognostics and health management Vibration Acoustics

1. Introduction Machine PHM has an important role in reducing maintenance costs as well as increasing safety by providing an accurate estimation of component or system damage to reduce machine break down. In addition, PHM predicts the machine’s health status without expensive regular inspections. Continuous machine health observation is based on dynamic procedures including the detection and the diagnosis of damage, and then prognosis of machine remaining useful life. Gearboxes represent one of the complex systems where PHM remains a challenging task. A wide range of features in different domains, especially from vibration sensors, has been studied and proposed for gear fault detection and diagnosis in the literature [1,2]. However, very few parameters have been evaluated for prognostic purposes in real systems. PHM techniques can be categorized based on the type of available data: (1) physics-based and (2) data-driven. Ozguven and Houser [3] presented a review on the different dynamic gear models based on bending stresses, contact stresses, pitting, scoring, transmission efficiency and estimation of loads on different gearbox elements such as bearings. The proposed models include variations in the effects and basic assumptions. Yao and McFadden [4] modeled a gearbox using a

n

Corresponding author. Tel.: þ1 613 991 9529; fax: þ1 613 949 8165. E-mail addresses: [email protected], [email protected] (A. Dadouche).

0888-3270/$ - see front matter Crown Copyright & 2012 Published by Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.ymssp.2012.05.013

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two-parameter (stiffness and damping) model in which the inertia of two gears in a one-stage gearbox with torsional vibration was reduced to one equivalent mass. Bartelmus [5,6] applied mathematical modeling and computer simulation to develop a gearbox dynamic model in support of signal evaluation for diagnostic inference. Some results of the computer simulations were also presented. It was shown that a flexible coupling and an error mode random parameter have an influence on the stability of the simulated gearbox. Badaoui et al. [7] examined a simplified mechanical model of geared transmissions and concluded that acceleration signals were approximately characterized by the convolution of the secondorder time derivative of a windowing function with impulse responses when a gear spall fault was present. Data-driven methods have been used extensively by a number of researchers. For instance, Ismall et al. [8] built a diagnostic model based on vibration data collected on a small-scale gear rig and collected vibration data to detect the gear fault. Generally, data-driven methods include frequency domain analysis [9,10], time-frequency domain analysis [11,12], adaptive regression models [13,14], neural networks [15–17], and support vector machine [18,19]. Lopez et al. [20] and Essawy et al. [21] used vibration data from different locations on a gearbox for condition monitoring. Their research fused data based on vibration signals and neural networks. Baydar and Ball [22], Mba and Rao [23], and Toutountzakis and Mba [24] investigated gear fault detection based on Acoustic Emission (AE) techniques. There are many advantages if a real system is available to generate a series of ideal data sets obtained on real machine components. The first advantage would be accessibility to a varied range of operating conditions in terms of speed and load. Secondly, the history and maintenance schedule would be available for the machine. This would provide further means for fault assessment. Therefore, to avoid excessive cost, the use of rigs simulating real environments and practical operating conditions would be a viable and preferred option. Operating in a more manageable environment allows detailed measurements with relative ease and at a reasonable cost. The objective of this paper is to introduce a newly developed gearbox test facility along with preliminary data obtained on a defected gear operating under controlled conditions with minimum disturbance to the accessory hardware. A test rig has been designed, built, instrumented, commissioned and experiments have been performed. Selected prognostic features are evaluated and discussed. 2. Gearbox test facility and instrumentation The GE J85 is a high thrust, lightweight, turbojet engine which was produced in different variants with a thrust ranging between 12.7 and 13.8 kN (2850–3100 lbf). The engine features a rotor supported by three rolling-element bearings and an eight-stage axial flow compressor coupled directly to two turbine stages. The test gearbox belongs to the J85 CAN40 engine variant which is still in service on the Canadair CT-114 Tutor. Generally, accessories for gas turbine engines can be divided into two categories: (1) those driven by bleed air taken from the compressor section of the engine and, (2) those driven mechanically by an accessory gearbox connected directly or indirectly to the engine main shaft. In general, the mechanical connection from the engine shaft may be through an engine-mounted gearbox or through a power take-off shaft to a remotely mounted gearbox. Fig. 1 presents a cutaway section of a J85 engine showing its main components as well as the accessory gearbox attached to the engine casing below the compressor section. The gearbox is characterized by four parallel shaftgears (Fig. 2) driving the various accessories mounted at each pad, as shown in the detailed schematic of Fig. 3. This gearbox variant is driven axially (axis B) via a transfer gearbox connected to the engine by an output shaft (axis A). Power is then transmitted directly to the over-speed governor and indirectly to the remaining accessories (starter/generator, hydraulic, fuel and lubrication pumps) through gears F, C and D. Rolling-element bearings are used to support and guide all shafts as shown in Fig. 3. Both ends of the main shaftgear (axis B) are splined to the transfer gearbox and over speed governor coupling shafts and could rotate at a maximum speed of 7811 rpm. At take-off, shaft gear B rotates at 6249 rpm (80% of its maximum speed), whereas its cruise speed is about 3671 rpm (47% of maximum speed).

Fig. 1. Cutaway section of J85 engine.

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Fig. 2. Test gearbox.

Fig. 3. Schematic of the test gearbox and accessories.

Table 1 Gear specifications. Shaft

Gear Number of teeth

Shaftgear B

Shaftgear C

Shaftgear D

B 49

C 54

D1 63

Shaftgear F D2 20

F1 24

Oil pump F2 24

OP 29

Shaftgear D drives the main fuel pump as well as the lubrication pump mounted on opposite pads of the gearbox. The fuel pump is driven through a spline connection and could run up to a maximum speed of 6075 rpm, whereas the oil pump is geared to axis D using an internal gear mechanism and could reach 4190 rpm at gearbox maximum speed. Shaftgear C is also driven by shaftgear B through the idler shaft F at a maximum speed of 7088 rpm. This shaft drives both the hydraulic pump and the starter/generator located at each of its ends. The three shafts are connected through splines to transmit power. Gear characteristics and rotational frequencies of each shaftgear are given in Tables 1 and 2, respectively.

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Table 2 Characteristic frequencies of shafts and gears. Speed

Maximum Take-off Cruise

Frequency, Hz Shaftgear B

Shaftgear C

Shaftgear D

Shaftgear F

Gearbox mesh

Oil pump mesh

130.18 104.15 61.19

118.13 94.50 55.52

101.25 81 47.59

265.79 212.63 124.92

6379 5103.2 2998.12

2025 1620 951.75

Fig. 4. General view of the gerabox rig.

2.1. Rig description A general view of the gearbox test rig (built and located at the National Research Council) is shown in Fig. 4. The gearbox and its accessories are mounted on the upper part of a duplex frame solidly bolted to the floor. Foamed rubber sheets were fitted between the upper and lower parts of the frame to protect the rig from any external vibration sources and also attenuate the response amplitude due to the transmission of reaction forces from the various rotating components of the gearbox and its accessories. For testing purposes, some changes have been made to the gearbox drive mechanism as well as the accessories to simulate real operating conditions. Therefore, the gearbox condition monitoring is performed under full load as indicated by [25,26]. Fig. 5 shows a schematic of the various modifications and a detailed explanation is provided below. One of the changes was to the drive system which is now composed of a 15 kW (  20 hp) electric motor connected to the gearbox (axis B) through a pulley/belt system and controlled using a variable frequency drive (VFD). The dimensions of the pulleys were selected to reach the required operating speeds while the VFD allows remote control of the motor speed and an accurate setting of the ramp rate. The oil pump installed on the right hand pad of axis D is a multi-element combined lubrication and scavenge vane pump. It is comprised of one pressure and five scavenge elements. The pressure element of the pump lubricates the accessory and transfer gearboxes as well as the engine main bearings through a system of internal channels, flexible pipes and nozzles which direct oil onto the various bearings and gears. Each scavenge-element picks up oil/air mixture from a

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Fig. 5. Schematic of the test gearbox and its accessories.

Fig. 6. Schematic of lubrication system: a. original, b. modified.

specified sump as shown in Fig. 6a. As the engine and transfer gearbox are not part of the test facility, some changes have been made to the lubrication system simulating real oil circulation in the engine and assuring a proper operation of the pump elements (Fig. 6b). The main modification was the connection of the pressure line to the scavenge pipes of the engine main bearings, Take-off assembly (bearings and gears), and transfer gearbox. Orifice restrictors have been installed in each scavenge line to maintain the proper oil flow rate through the scavenge elements of the pump. Since all sumps represent an oil/air mixture, a vent tube equipped with a check valve (open to air) was also added and connected to the scavenge pipes as shown in Fig. 6b. Due to some technical issues and a failure to reach the required fuel pressure at gearbox maximum speed, the fuel system was replaced by a truck alternator to keep the gearbox properly loaded at that pad. A battery and a load bank were connected in series to the alternator allowing the application of the required loads at different speeds. The load bank has five amperage switches allowing the application of various incremental electrical loads to the alternator depending on the operational speed, simulating real loads exerted by the fuel system on the gearbox. The alternator outputs a constant voltage of 14 V and the load bank can apply up to 107 A (amps) electrical load representing a total of 1.5 kW (2 hp) power draw. The hydraulic pump, installed at the left-hand pad of axis C, and its fluid circulation system were slightly modified for testing purposes. The pump was piped to the hydraulic oil tank forming a closed loop and assuring the pressure of the hydraulic oil leaving the pump is in the range of 9.65 MPa to 11.4 MPa (1400 psi to 1650 psi) at the different operational speeds.

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The starter/generator, mounted on the same axis as the hydraulic pump and on the opposite pad, plays two major roles: it starts the engine and it provides power to the various items of electrical equipment in the aircraft. It is important to mention that the starter/generator was used as a generator only, allowing the gears’ teeth to be loaded in the same direction over the testing period. A voltage regulator is attached to the generator to automatically maintain a constant output voltage level of about 28 V. The voltage regulator operates by comparing and adjusting the actual output voltage to an internal fixed reference voltage. A large capacity, multiple-switch load bank (up to 400 A) loads the generator based on an estimation of the total electrical power consumption of a typical aircraft (Falcon20) owned by the National Research Council. The aircraft is equipped with two CF700 engines that are derivatives of the J85 engine. The gearboxes are similar and the electrical load is very representative. The electrical load varies between 110 A and 140 A resulting in a power consumption ranging between 3.08 kW and 3.92 kW (4.13 hp to 5.25 hp). 2.2. Crack creation There are many types of gear failures with two representing the most common modes: (1) tooth root crack failure due to bending fatigue and possible inadequate bending strength, and (2) tooth pitting, also known as surface failure, due to the high compressive contact stress on the tooth surface profile. In this study, the former failure type was considered and investigated. However, collecting coherent gear lifetime data (healthy to failure) is a time-consuming and tedious task from an experimental point of view. This can be overcome by introducing an initial crack at the gear tooth root and running the gear to failure. Since the starter/generator pad was equipped with a wide range loading capability, gear ‘‘C’’ was selected for defect creation and remaining useful life and estimation. The fault was introduced after having performed a finite element analysis (using commercially-available software) on the gear in order to estimate the root bending stresses and to accurately determine the location of the maximum stress where a typical crack could initiate. The analysis was performed at different linear loads and were located close to the tooth tip and tooth mid-section. Centrifugal forces have also been accounted for in the FEM analysis. Fig. 7 shows the Von Mises stress distribution [27] for the case of a load of 134,150 N applied on the tooth tip and a speed of 3900 rpm. All studied cases showed a maximum stress located in the same vicinity. Based on these calculations, initial defects having 0.5 mm, 1.5 mm, 2 mm and 2.5 mm depth and 0.1 mm (0.004 in) width were created, each defect on a different spare gear (Gear C), at the gear tooth root, using electrical discharge machining (EDM). To reach a failure stage with the smaller defect, testing time would be very significant. Therefore, the first series of tests were carried out on the largest defect size (2.5 mm) in order to obtain data in a reasonable period of time, covering the phase of fault introduction to gear failure. 2.3. Instrumentation The test facility is equipped with instrumentation allowing the measurement of the overall gearbox performance such as vibration, acoustic emission, temperature, pressure, power consumption, speed and load. A high-speed, portable, 24 bit

Fig. 7. Stress distribution on tooth model.

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Fig. 8. Gearbox instrumentation.

Table 3 Gearbox rate of rotation. Operating condition

Cruise speed (SP1) Take-off speed (SP2)

Rotation, rpm Drive shaft (axis B)

Motor (VFD)

3671 6249

2050 3490

data acquisition system with a maximum bandwidth of 25.6 kHz was used to record the data. All dynamic signals (vibration and acoustic emission) were acquired at a sampling rate of 25.6 kHz, whereas the static characteristics (temperature, pressure and power) were recorded at a much lower rate. The gearbox signature/condition was monitored and tracked using three accelerometers and two acoustic sensors mounted on the gearbox casing close to the location of the gear with the defect as shown in Fig. 8. The accelerometers (two vertical and one axial) were stud-mounted on the casing. They are piezoelectric charge source accelerometers having a sensitivity of 9.8 pc/g and a frequency response range up to 12.6 kHz. Their mounted resonant frequency is 42 kHz. The vertical accelerometers will be denoted VA1 and VA2 and the axial one will be denoted AA. Two different types of acoustic sensors have been used: (1) a non-contact air coupled ultrasonic transducer (ACUT) and, (2) a contact acoustic emission sensor. The ACUT is a capacitive transducer with a thin film active element. Its frequency response range is up to 2.25 MHz with a 6 dB down point at the upper frequency. The sensor generates an output voltage proportional to any pressure wave arriving at its aperture. More details can be found in references [28,29] where the transducer showed very promising results for bearing fault detection. The contact acoustic emission sensor (R3a) was attached to the casing using a cyanoacrylate adhesive with a polyimide film backing. It features a resonant frequency of 29 kHz and an operating range of 25 kHz to 70 kHz at its peak sensitivity. However, the sensor can operate at lower or higher frequencies but with a lower sensitivity. The sensor is also characterized by a ceramic face which (electrically) isolates it from the structure assuring a low noise operation. The sensor converts the transient elastic waves generated by the release of strain energy (due to crack initiation/propagation) into an electrical signal. 3. Test matrix and preliminary data The test matrix was designed so as to simulate a flight profile of the gearbox. Data were recorded at all operating conditions which consist of: ramp up to take-off speed, stay at take-off speed for a few minutes, ramp down to cruise speed, stay at cruise speed for a number of hours, and then ramp down to stopped. The focus was on steady-state operation (constant speed and load) representing the cruise and take-off speed conditions. These two test situations represent 47% and 80% of gearbox maximum speed, respectively. Table 3 shows the rate of rotation at cruise and take-off

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Table 4 Load values for auxiliary systems attached to the gear box. Speed

Load (LO1) at cruise speed

Generator Truck alternator

Load (LO2) at take-off speed

Amperage, A

Voltage, V

Power, W

Amperage, A

Voltage, V

Power, W

110/140 7

28 14

3080/3920 98

110/140 100

28 14

3080/3920 1400

100

80

2FGM

60

|Amplitude|

|Amplitude|

100

1FGM

40 20 0

54

108 Order

162

216

2FGM 3FGM

0

54

108 162 Order

4FGM 216

0.2

|Amplitude|

8

|Amplitude|

1FGM

40

0

10

1FGM

6 4

2FGM

2 0

60

20

3FGM 0

80

0

54

108 Order

3FGM 162

0.15 0.1 1FGM

0.05

216

0

2FGM 3FGM

0

54

108 162 Order

4FGM 216

Fig. 9. TSA order spectra for SP1LO1 operating condition: (a) VA1, (b) AA, (c) ACUT, (d) AE.

speeds (denoted SP1 and SP2, respectively) for the gearbox and driving motor. However, this paper discusses data associated with cruise operating conditions only (speed and load) since this represents the regime at which the gear box runs most of its design life. Table 4 shows the applied load (fuel pump and generator pads) of the auxiliary systems at cruise and take-off speeds (denoted LO1 and LO2, respectively). The speed rates and the generator loads were provided by a sister laboratory possessing an aircraft equipped with CF700 engines derived from the J85 military engines. The test campaign consisted of a number of repeated conditions to benchmark the repeatability of the measurements simulating the engine environment. Fig. 9 depicts the time synchronous averaging (TSA) order spectra of the different sensors recorded during the seeded fault test at cruise speed. The spectra illustrate the presence of harmonics at the first, second, and third gear mesh frequencies. It can be seen that the dominant mesh frequency and number of harmonics differ for different sensors. Note that the defective gear has 54 teeth, so that the gear mesh orders are 54, 108, 162, and 216.

4. Defect to failure run (DTFR) and prognostic metric evaluation Defect to failure experiments were conducted to study the crack progression of gear C. The DTFR was operated for a total time of about twenty-nine hours representing five test series as shown in Table 5. For the first four hours (test 1), the gearbox was run at different speeds and loads and only few reference data were recorded and therefore will not be shown nor discussed in this paper. However, for tests 2 through 5, the gearbox was run at a constant speed (SP1) and load (LO1) and data were recorded on a regular basis over the testing period. For this test campaign, data were registered every twelve minutes for a time length of 30 s. Three consecutive repeats were performed for each data segment. The last test (test 5) was characterized by increasing the load (LO1) to its upper limit (140 A). The truck alternator load was kept constant for all tests. A threaded hole was machined in the casing to facilitate gear inspection using a boroscope; during experiments, the hole was kept capped. After each test (as shown in Table 5), the defect was inspected and photographed. Table 5 shows the duration of each test set, the number of data files and generator load amount. It is worth mentioning that each test run was performed on a separate day. After the completion of the 5th run, it was observed that the defective tooth was missing, therefore the DTFR was terminated. Fig. 10 depicts the gear before and after completion of the test

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Table 5 DTFR data features. Test number

Running hours

Number of data files

Generator load, A

Test Test Test Test Test

4 6 6 8.5 4.5

– 31 31 44 23

110 110 110 110 140

1 2 3 4 5

Fig. 10. Gear axis ‘‘C’’: a. crack size of 2.5 mm (before DTFR), b. missing tooth (after DTFR), c. Close-up of the tooth root (after DTFR).

Table 6 Commonly used metrics for gear fault detection. Defect detector

Equation

Zero-order figure of merit [34]

FM0 ¼ X peak-peak =

Sideband level factor [35]

Comment N P

RMSðf i Þ

i¼1

SLF ¼ ðSHFi1 þ SHFi þ 1 Þ=s

N  P Fourth order narrow band NB4 ¼ ðX nbenv X nbenw Þ4 =N =s4 metric [36] 1

RMS(fi): RMS values of all the amplitudes at the meshing frequency and its harmonics in the spectrum of the TSA SHF: first order sideband amplitudes around the fundamental gear mesh frequency s: standard deviation of the TSA Xnbenv: The envelope of the TSA band passed about the fundamental gear mesh frequency

series. In Fig. 10c, a close-up picture of the fractured section at the tooth root is presented. The figure clearly identifies the initial crack created at the tooth root and its propagation and development over the testing period (in particular the last test series). The latter shows the ductile crack propagation area due to fatigue as well as the brittle fractured area where the tooth was sheared off the gear. Generally, brittle crack propagation is characterized by its suddenness and smooth surface. All signals have been time synchronously averaged (TSA) to reduce the complexity of the signals [30–32]. This method is particularly useful for gear fault diagnosis. It requires simultaneous recordings of signals along with a tachometer signal that is synchronous with the shaft of interest. Gear defects can be more readily detected from the synchronously averaged time signal. However, in case the acquired signals are not long enough to perform TSA (i.e., low speed machinery), other methods could be used to extract fault diagnosis features like specteral kurtosis as stated by [33].

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There are many proposed gear fault diagnostic features in the literature and some of the most common have been selected for prognostic purposes in this study. There are also several basic signal processing metrics that can be applied to gear fault detection such as mean value, standard deviation (Std), kurtosis, and skewness. These features have been extracted from the TSA signals to verify the quality of the test rig and available instrumentation. Table 6 presents some of the most commonly used fault detection parameters which will be discussed. Features like FM0 are derived from residual signals whereas indicators like NB4 are derived from the envelope of the band passed TSA. The trends of these metrics should help in defining the onset of failure or gear seizure. It should be mentioned that the features considered in this paper are not comprehensive-they have been short-listed from the extensive features found in the literature and chosen because they represent the most basic techniques applied in industry for gear defect prognostic purposes. Figs. 11–15 show the trends of the prognostic metrics. It is important to mention that these metrics were extracted from the data collected over test series 2 through 5. Fig. 11 indicates the changes in the standard deviation of the vibration and acoustic sensors over the testing time. For the first 31 data points (test 2) framed in Fig. 11a, the parameters are very

200

Std. deviation

Std. deviation

200 150 100 50 0

0

20

40

60 80 100 Observation

120

50

0

20

40

60 80 100 Observation

120

140

120

140

0.8

10

Std. deviation

Std. deviation

100

0

140

12

8 6 4 2

150

0

20

40

60 80 Observation

100

120

0.6

0.2 0

140

X: 112 Y: 0.2698

0.4

0

20

40

60 80 100 Observation

1

1

0.5

0.5

Skewness

Skewness

Fig. 11. Std metric: (a) VA1, (b) AA, (c) ACUT, (d) AE.

0 -0.5

-0.5 -1

40 50 60 70 80 90 100 110 120 130 140 Observation

1

1

0.5

0.5

Skewness

Skewness

-1

0

0 -0.5 -1

40 50 60 70 80 90 100 110 120 130 140 Observation

40 50 60 70 80 90 100 110 120 130 140 Observation

0 -0.5 -1

40 50 60 70 80 90 100 110 120 130 140 Observation

Fig. 12. Skewness metric: (a) VA1, (b) AA, (c) ACUT, (d) AE.

40

40

30

30

SLF

SLF

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20 10

20 10 0

40 50 60 70 80 90 100 110 120 130 140 Observation

40

40

30

30

SLF

SLF

0

309

20 10

40 50 60 70 80 90 100 110 120 130 140 Observation

20 10

0

0 40 50 60 70 80 90 100 110 120 130 140 Observation

40 50 60 70 80 90 100 110 120 130 140 Observation

4

4

3

3

FM0

FM0

Fig. 13. SLF metric: (a) VA1, (b) AA, (c) ACUT, (d) AE.

2 1

1 0

40 50 60 70 80 90 100 110 120 130 140 Observation

4

4

3

3

FM0

FM0

0

2

2 1 0

40 50 60 70 80 90 100 Observation

120 130 140

2 1

40 50 60 70 80 90 100 110 120 130 140 Observation

0

40 50 60 70 80 90 100 110 120 130 140 Observation

Fig. 14. FM0 metric: (a) VA1, (b) AA, (c) ACUT, (d) AE.

scattered, indicating that the system did not reach its steady state regime at the set operating conditions-a normal situation in gearboxes. Therefore the features covering the test 2 timeframe (day 2) will be omitted in Figs. 12 through 15. Also, the beginning of each test is characterized by a few scattered peaks as shown (for instance) around the 62nd to 65th data points associated with the beginning of the test on the third day. The 107th data point (dashed lines in Fig. 11) indicates the time at which the generator load was increased from 110 A to 140 A. After increasing the load, the Std metric decreases for all signals. However, Stds of the ACUT and AE show a certain sensitivity to overloading with a steeper decay compared to the accelerometers. As Fig. 11(d) shows, there is a peak at the 112th data point (about one hour after increasing the load), an indication of a change in the gearbox condition which could be attributed to the removal of the tooth. On the other hand, no sign of gradual crack progression was observed. Fig. 12 shows the skewness value of the TSA signals excluding the second data set (test 2). The ACUT skewness value, as shown in Fig. 12c, increases monotonically. This could be an indication of the different stages in crack progression over the test period. Increasing the load leads to a slight increase in slope, especially around the 112th data point where the tooth could have been removed. The scattered data of the acoustic emission skewness (Fig. 12d) over the last test series could

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2

2

1.5

1.5

NB4

NB4

310

1 0.5

0.5 0

40 50 60 70 80 90 100 110 120 130 140 Observation

2

2

1.5

1.5

NB4

NB4

0

1

1 0.5 0

40 50 60 70 80 90 100 110 120 130 140 Observation

1 0.5

40 50 60 70 80 90 100 110 120 130 140 Observation

0

40 50 60 70 80 90 100 110 120 130 140 Observation

Fig. 15. NB4 metric: (a) VA1, (b) AA, (c) ACUT, (d) AE.

also be associated with a regime change of the gearbox. Metrics with zero average slopes or without a consistent trend are difficult to interpret as is the case for the accelerometers skewness values (Figs. 12a and b). SLF metrics of the different sensors are presented in Fig. 13. The vertical accelerometer (VA1) and ACUT are slightly affected by the load increase as SLF shifts to higher values but remains steady. The axial accelerometer and AE are not affected by the load change nor by the tooth extraction, therefore their SLF values may be unsuitable prognostic metrics. Similarly, the trends of the vibration FM0 remained unchanged over the complete series of tests as shown in Figs. 14a and b. The abrupt change at the 107th data point represents a combination of the beginning of the last series of tests (test 5) and gearbox overloading. It is important though to mention that the ACUT FM0 remained almost steady at the lowest load but started increasing monotonically as the load increased, a sign of gearbox condition change. The AE shows a shift of its FM0 to lower values when the load changes. The NB4 metric of all sensors is illustrated in Fig. 15. The trends of this feature are inconclusive for all sensors as no substantial change was observed except for ACUT where NB4 shifts downwards at the beginning of test 5, remains steady, and then starts climbing steeply with time indicating a gearbox regime shift. Since this analysis is based on data driven technique, the general trends could be considerably different if applied to a different system. For example, in other systems with more interference and noise sources, more advanced signal processing would be needed, and the chosen metrics might respond differently to load changes and component fault progression.

5. Conclusion In this paper a detailed description of a complex aircraft gearbox test rig was given. Significant modifications have been brought to the different accessories to assure suitable loading and proper operation simulating real operating conditions. To validate the designed test rig, defect to failure tests have been performed where a variety of sensor technologies were used and selected prognostic metrics were evaluated. The main conclusions of this study are reported below: – The weight and magnitude of the prognostic features change depending on the sensor-technology as well as sensor location on the gearbox casing. – As opposed to vibration transducers, the standard deviation of the ACUT and acoustic emission sensors exhibited interesting trends over the testing period where the tooth was removed. – Skewness and FM0 metrics of ACUT showed very promising results. The skewness metric increases monotonically (a sign of crack propagation) until the complete removal of the tooth where the increase becomes steeper. Similarly, FM0 also shows a monotonic increase over the period where the tooth went missing. The trends for the other sensors were not conclusive. – NB4 trends do not show any sign of fault growth or tooth removal except for ACUT which shows a sign of anomaly after the tooth was extracted. This requires more study and analysis. – To examine the effect of load on the gearbox response while operating within the design limits, the generator load was increased from 3.08 kW to 3.92 kW which accelerated the gear C DTFR. Sensors/features responded differently to the load change.

A. Rezaei, A. Dadouche / Mechanical Systems and Signal Processing 33 (2012) 299–311

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– To improve the accuracy and efficiency of the remaining useful life (RUL) prediction, important sensory signals must be carefully selected to characterize degradation for engine components health prognostics. Future work and data analysis will be focused on data fusion from the various sensors to estimate the current condition of the damaged gear and to what extent the gearbox system can handle a soft failure.

Acknowledgment The authors would like to thank Defence Research and Development Canada for partially funding this research. The authors would like also to express their gratitude to Marcel Fournier, Brian Liko, Randy Payette, and Jean-Pierre Bedard from the NRC Gas Turbine Laboratory for their hard work and contributions to build and commission the test rig. Special thanks also go to Joe Maillet from Department of National Defence as well as Andrew Pang from Magellan Aerospace for their technical help and provision of test and spare gearboxes. References [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] [14] [15] [16] [17] [18] [19] [20] [21] [22] [23] [24] [25] [26] [27] [28] [29] [30] [31] [32] [33] [34] [35] [36]

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