Use of Artificial Intelligence Methods for Advanced Bearing Health Diagnostics and Prognostics S.L. Chen1, Mark Craig1, Rob Callan2, Honor Powrie2 and Robert Wood1 1
Surface Engineering and Tribology Group, School of Engineering Sciences, University of Southampton, Southampton, Hampshire, SO17 1BJ, UK 2
GE Aviation Digital Systems, Information Systems School Lane, Eastleigh Hampshire, SO53 4YG, UK
[email protected] Abstract—Prognostics is the ability to predict the condition of a piece of equipment at any stage during its useful life. It is the cornerstone of Prognostics Health Management (PHM), major goals of which are efficient maintenance and logistical practices, and optimized mission or equipment use and effectiveness. PHM will be achieved through monitoring a range of equipment sub-systems and combining the information to predict how and when the equipment will fail, with sufficient time for action or planning. This paper describes ongoing research by the University of Southampton and GE Aviation to investigate the intelligent processing of mechanical component health data to improve prognostics and diagnostics: In particular to evaluate the effectiveness of various sensing technologies (when applied to monitoring bearings), extending the window of time over which a failing component condition may be determined (prognosing) and identifying the nature of the failure (diagnosing).12
program, jointly funded with the US Air Force Research Laboratory (AFRL). The ProDAPS toolset incorporates various Artificial Intelligence (AI) based technologies, which have been developed for analyzing aircraft health data. The toolset has two core tools: data mining (incorporating advanced learning algorithms) and reasoning (probabilistic networks), and provides capabilities including data mining, anomaly detection, information fusion, reasoning and decision support / action planning for a variety of applications (see [1] and [2] for example). In the work described herein, the ProDAPS toolset is used to model the density of the data. Additional techniques, including Hotelling’s T-squared statistic, extreme value statistic (EVT), log-likelihood score and Entropy Score, are then applied to the model to extract prognostic and diagnostic information. The analysis uses data collected from a series of bearing rig tests conducted by the University of Southampton, UK. The test rig instrumentation included vibration and electrostatic sensors as well as off-line processes such as debris analysis and tribological assessment of bearing condition, which may be used for corroborative purposes. The various processed parameters from the electrostatic and vibration sensors are used as the input for the intelligent data analysis. Two data sets are used: no fault (baseline) data and data from an accelerated bearing failure (seeded fault) test. The no fault data is used for training and the methodology developed is applied to the seeded fault data. The paper describes the approach taken and techniques applied to characterize the no fault data, including how to deal with ‘natural’ variation in the processed sensor parameters. When applied to the accelerated bearing failure test, the intelligent analysis results demonstrate how the prognostic window is significantly improved relative to the original processed parameter indication and also the relevance of each processed parameter for increasing the early indication (or precursor) of the failure. The approach also identifies the
TABLE OF CONTENTS 1. INTRODUCTION ...................................................... 1 2. ADVANCED DATA ANALYSIS ................................. 2 3. BEARING RIG TESTS .............................................. 2 4. RESULTS ................................................................. 3 5. CONCLUSIONS ........................................................ 8 REFERENCES ................................................................. 8 ACKNOWLEDGEMENTS ................................................. 8 BIOGRAPHY ................................................................... 8
1. INTRODUCTION GE Aviation has developed intelligent tools and techniques under the Probabilistic Diagnostic and Prognostic System (ProDAPS) dual use science and technology (DUS&T) 1 2
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main variables (extracted features) which drive the anomaly detection so as to provide diagnostic information.
many applications as they represent the extremes of normal events and a means by which novel events may be defined.
2. ADVANCED DATA ANALYSIS
Log-likelihood score measures how well an established GMM fits the test data. The higher the log-likelihood, the better the fit.
Feature Extraction Real-time signals from electrostatic wear-site (WSS) and vibration sensors were processed into a series of features. These features form the input to the AI based processing. The features produced for the sensor types are given in Table 1. An overview on the electrostatic technology and processing may be found in [3].
Mahalanobis Distance Mahalanobis distance is used to measure the similarity between the unknown test sample and known Gaussian mixture. The less the distance the closer the similarity.
3. BEARING RIG TESTS Table 1. Feature Extraction for the On-Line Sensors Feature Vibration Electrostatic WSS D D RMS D D 1 / Rev D D Cage D D Roller D D Outer Race D D Inner Race
The bearing rig tests discussed in this paper form part of a series of tests undertaken at the University of Southampton. Details of the rig may be found in other references (see [4] for example), so only a brief overview is provided here.
Gaussian Mixture Model (GMM) Often it is assumed that data may be described by a single, normal Gaussian distribution. However real world data may be distributed in a more complicated way. Data from different time intervals might occupy their own space of normality (e.g. vibration level varies at different times due to a different load regime) or contain unknown anomalies. Clearly data with these elements will not follow a single Gaussian distribution. In this case, the data may be characterized by a combination of Gaussian distributions, or a Gaussian Mixture Model. Anomaly Detection Often data sets, which are believed to be fault-free, contain a degree of fault or poor integrity data. If such a data set is used to develop anomaly detection then the method will generally be flawed, the anomaly model being de-sensitized to anomalies inherent in the training data. For the current work, a Gaussian Mixture Model was used to model training data and construct the anomaly detection model. The entropy statistic was calculated for each cluster within the GMM. Poor integrity training data was identified from clusters with the lowest entropy scores. These were removed from the original GMM and the anomaly model rebuilt from the remaining data. Fault Detection Indices Hotelling's T-squared Statistic is a statistic for a multivariate test of differences between the mean values of two groups. Extreme Value Theory is a branch of statistics concerning data with unusually low or high values i.e. data that lies in the tails of the distribution. These points are significant in 2
The bearing rig comprises four taper roller bearings housed in a chamber, as shown in Figure 1. The bearings are mounted on a shaft driven by an electric motor. The outer two bearings (#1 and #4) are support bearings, whilst the inner bearings (#2 and #3) are test bearings. The two test bearings have radial load applied to accelerate their failure. In addition, for the failure test analyzed in this paper, bearing #3 was pre-indented on the inner race. The baseline test was with all non-indented bearings. The bearing types were LM67010 (cup) and LM67048 (cone), which are all steel bearings. The rig was run at a constant speed of 2500 rpm under ambient conditions. The rig was instrumented with a number of condition monitoring sensors. In the test chamber there is one vibration sensor (mounted externally), three electrostatic wear-site sensors (WSS) - one each on bearings #1 and #4 and one looking at bearings #2 & #3 - and two thermocouples monitoring the outer raceways of bearings #2 and #3. The oil re-circulation pipework included an electrostatic oilline debris sensor, thermocouples to measure temperature at the inlet and outlet of the test chamber and ambient temperature. Additional oil-line debris sensors included an inductive debris sensor, which provided particle counts for > 100 microns. The testing also included a number of complimentary offline analyses. Oil samples were taken during various stages of the testing and these were analysed for sub-100 micron debris content. Tribological analyses of the bearing condition pre and post-test were conducted and included photographic evidence and mass loss calculations. Where applicable, the oil-line sensors and post-test / off-line analyses were used to help interpret the responses of the vibration and WSS sensors during the tests.
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Figure 1 - Bearing Rig Chamber Showing Main Test Components 0.2
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4. RESULTS Baseline Test Examples of the feature extraction data, from the baseline test, for the vibration and electrostatic sensors are presented in Figure 2. From these RMS levels, an initial ‘run-in’ phase can be seen, where the signals start off relatively dynamic. The run-in phase is associated with the production of fine debris from the new bearings, which reduces with time. This feature would be expected from all tests utilizing new bearings and has been corroborated by the results from the 3
oil-line debris sensors. Once run-in has completed, the feature levels reduce to a nominally steady state. The feature extraction data from the baseline test was used as the input to the AI based anomaly detection process described in Section 2. Initially all the baseline data was run through the model but it was evident that certain sections of the data were exhibiting atypical behaviour. Including these in the anomaly detection model would lead to a coarser view of ‘normality’ which may not prove so effective when
trying to detect real faults. For this reason the baseline data was trimmed and a new anomaly model generated. This was used in the analysis of the seeded fault test data. Failure Test The failure test lasted approximately 63 hours and resulted in the unexpected failure of one of the support bearings, as opposed to the seeded fault bearing. Fortunately, sufficient instrumentation was in place on the rig to enable the real bearing failure to be comprehensively monitored. The extracted features from the failure test show responses to the impending bearing failure from the electrostatic (WSS) and vibration sensors from about 54 hours. Examples of these responses are shown in the RMS data from the vibration and WSS sensors, in Figure 3. From this it is evident that a significant bearing failure event is in progress but, even with the different extracted features, it is not certain exactly which bearing component (i.e. inner race, outer race, roller or cage) is the cause of the event. Possibly because the failure is so significant at this point many of the features are affected and therefore no clear pattern is emerging. It is noted that the start of the event is within less than 10 hours of the failure, which in real terms may not be sufficient time to determine the appropriate course of action (inspect, diagnose, determine maintenance action, obtain spares etc.) Thus, whilst detection of the impending failure is clear, there are two aims behind further analysis of the data. First
is to determine whether an earlier (prognostic) evaluation of the failure can be achieved, i.e. can the failure be detected in advance of the final few hours? Second is to see whether there is any capability for determining what the fault actually is i.e. is there any clear diagnostic information available? In the field, both these elements of information are important if goals such as optimized maintenance planning, minimum logistic footprint and maximum equipment usage are to be achieved. This is true for commercial and military applications. The feature extraction results from the failure test were further analysed using the AI techniques described earlier. Figure 4 shows Hotelling’s T-squared statistic data, which has been run through the anomaly detection model. From this figure there is evidence of abnormal behaviour at the start of the test which, as already observed from the baseline test, is due to the bearing running in. The run out to failure from about 54 hours, dominates. There is evidence of change in advance of 54 hours: an overall increase at around 36 or so hours, with significant peaks between 43 and 45 hours. This is shown in more detail in Figure 5, where the initial run-in and final gross failure results have been left out. The ‘in between’ data set is more representative of day-to-day running and shows the challenges of detecting significant changes in bearing condition amidst the normal variability. For the remainder of the data analysis presented, the initial run in and final failure results have been omitted.
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Figure 5 - Anomaly Detection from Hotelling’s T2 Statistic with Run-in and Gross Failure Data Omitted
From the anomaly analysis, a contribution value for each extracted feature was determined. This shows which features are most significant in driving the anomaly. Looking first at the RMS contribution values, it can be seen that both the electrostatic and vibration sensor data play a role in the anomaly detection (Figure 6). Drilling down to 5
the extracted features, for the WSS 3 the larger contribution values come from the Outer Race (OR) at around 37 and 43 hours (see Figure 7). The WSS Rolling Element (Rlg El) contribution values from about 36 hours onwards are also increased (Figure 7). This ties in with the initial rise in the T-squared anomaly at that time.
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Figure 8 - Vibration Sensor OR Contribution Values For the vibration data, the significant contribution values come from the OR component from 37 hours onwards (Figure 8). The AI based analysis of the data has shown that a significantly earlier detection may be achieved from the vibration and electrostatic sensors, increasing the time from detection to failure by approximately 18 hours (i.e. from 9 hours to 27 hours). It should be noted that this was an accelerated failure test, so the time to failure will be significantly reduced compared to an in-field operated bearing. The additional analysis has also demonstrated the potential for diagnosis of the impending failure: Both vibration and electrostatic WSS data indicate a failure in the bearing outer race. The electrostatic sensor also detects a problem with the rolling elements. As both sensors indicate the outer race damage, it would be expected that this is the more significant of the two faults. Both these faults and the hence the diagnosis are corroborated in the post-test tear down and inspection of the bearing: There is significant damage to the outer race (Figure 9) and several of the rolling elements suffered material loss as well (Figure 10). Although not undertaken for the work presented, it is envisaged that this type of information could be fed into a reasoning network to enable the nature and severity of the predicted faults to be automatically determined.
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Figure 9 - Post-test Condition of Failed Bearing Outer Race
Figure 10 - Post-test Condition of Failed Bearing Rolling Elements
To put this into context, ProDAPS has an advanced probabilistic reasoning engine with unique capabilities for scaling to large tasks and representing sophisticated statistical models. The reasoning engine is used to provide more information about an anomalous event such as the likely cause and it can assist with difficult decisions by quantifying the cost and benefits of different actions such as maintenance. Reasoning can be inferencing with a-priori knowledge or case-based by matching to previous cases with similar symptoms. The reasoning engine is able to combine the strengths of data driven modelling (knowledge derived from experience) with a-priori knowledge. This paper has explained a data driven analysis technique for detecting an abnormal event that gives warning of an impending failure and extracts the processed sensor features contributing to the anomaly. The reasoning engine can use a-priori knowledge about the response of these features to different failure modes to predict the type of failure. The same procedure, but using different anomaly modelling and contribution scoring of input features, is currently being applied to Health and Usage Monitoring System (HUMS) vibration data collected from helicopters operating in the North Sea. To date, ProDAPS has highlighted many events not seen by the conventional HUMS threshold detection analysis. Models for reasoning about the anomaly outputs are currently being developed and these reasoning models will have the task of distinguishing the type of fault (e.g. mechanical component or instrument) and the nature (diagnostic) of the failure (e.g. shaft imbalance). A similar architecture is being implemented for processing electrostatic debris detection data on the GE F136 engine for the Joint Strike Fighter (JSF) aircraft.
efficiency, maximized use of equipment, reduced logistic footprint, optimized maintenance, decision support etc. are to be realized, then continued development of advanced analysis techniques such as those discussed herein are essential.
REFERENCES [1] Rob Callan and Brian Larder, “The implementation of advanced diagnostic and prognostic concepts ─ Practical tools for effective diagnostic and prognostic health management” IEEE Aerospace Conference, Big Sky, Montana 2003, ISBN: 0-7803-7652-8. [2] Rob Callan, Brian Larder and John Sandiford, “An Integrated Approach to the Development of an Intelligent Prognostic Health Management System” IEEE Aerospace Conference, Big Sky, Montana 2006, ISBN 0-7803-9546-8. [3] H. E. G. Powrie, R. J. K. Wood, T. J. Harvey, L. Wang and S. Morris, Electrostatic charge generation associated with machinery component degradation, IEEE Aerospace Conference, Big Sky, Montana 2002, ISBN 0-7803-7232-8. [4] T. J. Harvey, R. J. K. Wood and H. E. G. Powrie, “Electrostatic wear monitoring of rolling element bearings”, Wear, Volume 263, Issues 7-12, 10 September 2007, Pages 1492-1501.
ACKNOWLEDGEMENTS CONCLUSIONS The results presented in this paper have shown that AI based analysis techniques have a significant role to play in optimization of equipment management. Traditional feature extraction data, from vibration and electrostatic sensors, have been exploited to provide prognostic (i.e. increased time from detection to failure) and diagnostic information (i.e. determine the nature and possibly severity of the impending failure). The difference in the performance of the sensor data with and without the additional analysis is clearly highlighted. The diagnostic results presented relate to sub-components of the bearing. It is recognized that this level of diagnostic would generally be too detailed for most fielded applications; the purpose of the current approach is to show that a corroborated diagnostic can be achieved from different sensor outputs. In real world applications, the approach may be scaled up to produce diagnostic information at equipment sub-system level, as appropriate. This paper has presented work in progress, however it is evident that if operational goals such as improved 8
The authors would like to thank all those involved in setting up and running the bearing rig tests, which provided the data for the current analysis and also those involved in developing the ProDAPS toolset that has been used to analyse the data.
BIOGRAPHY Rob Callan is a technical fellow at GE Aviation Information Systems. He holds a BSc in Mechanical Engineering and a PhD in Artificial Intelligence. Rob has over 20 years experience in AI, with 11 of these spent in aerospace applications. During the past 8 years, he has been technical lead for the development and application of AI techniques for PHM. The techniques concern the extraction of information from data and span the PHM functional layers from state detection to decision support. Capabilities are state-of-art in the areas of anomaly detection, reasoning / fusion and decision support. These techniques are now being applied to helicopter health management and military engine diagnostics / prognostics. Rob has published many academic papers and two textbooks on AI.
Honor Powrie is a Technology Manager at GE Aviation Information Systems, specializing in the application of electrostatic sensors for condition monitoring. She has a wide interest in condition monitoring practices including the fusion of multiple data sets to improve fault diagnosis and prognosis. Honor holds a BSc in Physics and a PhD in Aeronautical Engineering.
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