Gearbox failure prediction using infrared camera Chiman Kwan *, Roger Xu, and Len Haynes Intelligent Automation, Inc. ABSTRACT This paper summarizes our results on gearbox failure prediction using infrared camera. Experimental data was taken at the Mechanical Diagnostic Test Bed (MDTB) of the Penn State University. It was observed that there is thermal growth before a gearbox failure occurs. One explanation is that friction between gears increases before the gear breaks. A neural net based image-processing tool was developed by Intelligent Automation, Inc. (IAI) to process the infrared images in real-time. Results show that our tool can detect unusual thermal growth five hours before one of the gear teeth was broken.
1. INTRODUCTION Nondestructive evaluation of electromechanical structures is important in many applications. Current research directions have been looking for techniques that could predict the presence of faults before they actually happen. Under the support of ONR, IAI and its partner company, Thresholds Unlimited, Inc. (TUI), worked together to seek the feasibility of using infrared camera for gearbox failure prediction. TUI was responsible for data acquisition and IAI was responsible for software development and testing. The gearbox setup was the one at Penn State University as shown in Fig. 1. The Mechanical Diagnostics Test Bed (MDTB) at Penn State U. was constructed to provide data on a transmission such as gearbox as its health progresses from new to faulted and finally to failure. These data sets will be crucial in the development of prognostic techniques for Condition Based Maintenance. The gearbox is driven at a set input speed using a 30 Hp, 1750 rpm AC induction motor, and the load torque is applied by a 75 Hp, 1750 rpm AC induction motor. The speed variation is accomplished by varying the supply frequency to the motor.
Fig. 1 Mechanical Diagnostics Test Bed (MDTB) at Penn State University The images were recorded by a Mitsubishi infrared sensor and the images were stored in VHS tapes. IAI was able to process the video images in real-time (3 seconds a frame) and detected early thermal growth in gearbox 5 hours before an actual failure occurred. The paper is organized as follows. In Section 2, we will describe a real-time image processing tool for gearbox failure detection. Section 3 will summarize our results. Finally, future research will be briefly mentioned in Section 4.
2. A REAL-TIME IMAGE TOOL FOR FAILURE PREDICTION USING INFRARED IMAGES *
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A raw thermographic image is shown in Fig. 2. It is a black and white image. The pixel magnitude represents temperature where black means hot. According to our collaborator Charlie Weinert at TUI, there are two observations before a failure such as a broken tooth occurs. First, there is an increase in temperature near the axle area on the left of the image as indicated in the image. This is a result of heat propagation from gearbox through the steel axle. Before a tooth breaks, there is heat building up in the gearbox because of misalignment and friction between gears. Then heat is building up in the gearbox and then propagating to left (axle area) and right (right side of the image). Second, after the failure occurred, the temperature tends to cool down. This second observation can be explained by noting that once a tooth is broken, the tension between gears is reduced. As a result, less friction and less heat are being generated. Charlie Weinert at TUI has observed similar phenomenon in rail wheels.
axle
Fig. 2 A raw infrared image taken by the Mitsubishi camera. IAI developed a software tool to automatically identify the pre-failure heat growth in the gearbox thermal images. Our hardware consists of a VCR playing the thermal image video tape, a frame grabber known as TurboTV, and a PC that processes the video images in real-time. The processing of image is in real-time, i.e. 1 frame in every 3 seconds. For thermal signature recognition, this is almost real-time since the thermal growth is slow and gradual. The screen shot shown in Figure 3 shows the user interface of our program. There are two windows showing the performance indices of two approaches: 1) conventional image processing technique; 2) a neural net approach using Principal Component Analysis. Just below the two performance indices, there are two windows of images showing a normal thermal profile when there is no failure and an image showing changes when there is abnormal thermal growth in the system. We will describe the two approaches below. Both approaches require the generation of a normal thermal image profile. This normal image profile can be obtained by using the first few cycles of the experiment since it is assumed the first few cycles contain normal images where no failure occurs. Each cycle consists of approximately two hours of time. In each cycle, the load of gearbox is ramped down from 300 % normal load down to 100 % of load. The normal image is obtained by capturing the maximum pixel magnitude in each pixel location. On the top left of Figure 3, there is a button that indicates the system status. If the thermal growth is beyond certain threshold then this button becomes red. Otherwise, it is gray in color. There are also several control buttons such as “load profile”, “begin training”, etc., are for training and monitoring of infrared image on-line. They are self-explanatory. We now describe the two approaches of real-time thermographic image processing for gearbox failure prediction.
Area 1
Fig. 3 IAI’s real-time health monitoring software interface.
Area 2
Fig. 4 Two areas that show significant thermal activities.
Approach 1: Conventional Image Processing Technique This technique computes the area of growth in two regions of the thermal image as shown in Figure 4. Since the infrared camera is at a fixed distance, every image has the same size. Therefore, if we locate the regions of interest, then we can count the number of pixels that have increases in magnitude. The process involves a comparison between a sample frame with the stored normal image. If the pixel magnitude in the newly acquired frame is larger than that of the corresponding pixel in the normal image, then the area count is increased by 1. As the area grows, it is visually reflected in the form of pseudo colors in the lower right window of Figure 3. It is also reflected in the performance index. For a healthy gearbox, the performance index is set to 1. If an increase in thermal area in the thermal image, then the performance index is dropping, indicating a decrease in health of the gearbox. This performance index is extremely useful in prognostics since one wants to know not only the existence of failure but also the degree of degradation of the system. Approach 2: Real-time prediction of health status of gearbox using PCA neural network PCA is a powerful technique for feature extraction and characterization. Figure 5 best illustrates the key ideas of PCA. Step 1 is the formation of a U matrix whose elements are the eigenvectors corresponding to the large eigenvalues in the correlation matrix. The second step is a projection step to extract the features out of a sample vector. Step 1: Form U matrix
Sample vectors from all classes
Calculate correlation matrix R = E(xxT)
Determine the eigenvectors of the largest Q eigenvalues of R
Form U matrix
Step 2: Calculate the principal components Sample vector x
UT
Feature vector of smaller dimension
Figure 5 Basic principle of PCA. The most important application of PCA is for dimensionality reduction1. We may reduce the number of features needed for effective data representation by discarding those linear combinations that have small variances and retaining only those terms that have large variances. Here we briefly describe how we used PCA for degradation measurement of gearbox. There are several key steps in the process. Step 1: Divide the normal image into small subimages and compute the eigenspace of the subimages The normal thermal profile of the gearbox is divided into subimages of 10x10 pixels. The key in this step is to generate the eigenspace of the subimages. For each subimage, there are 100 pixels and they are arranged into a single vector. There are a total of about 300 subimages in the normal thermal profile image, i.e. we have 300 vectors. PCA is a self-learning type of neural network. One just needs to present the 300 vectors to the PCA network, the learning algorithm can automatically find out the eigenvectors and eigenvalues of the signal space. Step 2: Divide the sampled frame into small subimages and project each subimage into the eigenspace of the normal image Once the eigenspace of the normal thermal profile is determined, we need to compute the projections of each sampled frame from the VCR to the eigenspace of the normal thermal profile. Here we divide each sampled frame into subimages of size 10x10. So we have about 300 vectors for each frame. Then we form inner products between the 300 vectors with the eigenvectors generated from the normal thermal profile. The outputs of the inner products are called the principal components. We selected 10 components to characterize each subimage. Step 3: Compute the differences between the principal components of each subimage
Ten principal components were used to characterize each subimage. For each newly acquired frame, we compute 10 principal components and then compare the 10 components with those in a corresponding subimage in the normal thermal profile. The difference between the components will indicate whether there is a change inside that particular subimage. Step 4: Generate a performance index that shows the health status of the gearbox Finally, a performance index is generated to show the health status of the gearbox. Basically, the performance index is the root-mean-square of the differences between all the subimages in a sampled frame from VCR and the subimages in a normal thermal profile. If the difference is very small, the performance index will be close to 1. Otherwise, it will be smaller than 1. If there is significant growth in the gearbox image, the index will be 0.6 or 0.5, indicating a significant degradation in the health status of the gearbox.
3. RESULTS We have tested the software by playing different portions of the video tape. Our software was able to detect thermal growth 5 hours before the actual failure of the gearbox. Comparing the two performance indices, the PCA neural net approach is more robust to sudden changes in the environment. In other words, if there is some gaussian noises in the thermal image, the performance index will not change abruptly. The following figures summarize the results of our approach. Figure 6 shows the system status at 9:58 pm. Failure of gearbox occurred at a time between 2 to 3:30 am the next morning. The performance indices indicate that the system health status was degrading. Both indices show values of 0.85. Actually the system started to degrade about 7 pm. Figure 7 shows the system status at 0:51 am. The performance indices are at 0.78. By visually inspecting the thermal growth, it can be seen the area has grown a slightly over the past three hours. Figure 8 shows the system status at 1:19 am. Now the thermal growth has picked up significantly. As can be seen from the colored images, the thermal growth area has grown quite significantly over the past 30 minutes. Figure 9 shows the system status at 2:04 am. Now the thermal growth has reached near its maximum. The performance indices decreased a little bit from 0.6 to 0.55 over the past 40 minutes. Since our software captures the maximum pixel intensity at each pixel location, we were not able to observe the decrease in temperature after failure occurred. It is speculated that a broken tooth occurred between 2 am and 3:30 am.
Fig. 6 Thermal growth has been observed at 9:58 pm.
Fig. 7 Thermal growth area is still increasing at 0:51 am.
Fig. 8 Temperature is still rising at 1:19 am.
Fig. 9 Temperature reaches near its maximum at 2:04 am.
4. FUTURE RESEARCH DIRECTIONS In our tests, we played back the infrared images from the VCR and applied our tool in real-time to process the images. This is still not real experiment. Future work is necessary to further test the tool under various environmental conditions. Most importantly, we need to check the robustness of our tool with respect to various loading conditions and environmental variations.
ACKNOWLEDGMENTS This research was supported by Office of Naval Research under contract N00014-97-C-0454.
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