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Van Hecke B.,University of Illinois at Chicago | Qu Y.,DEI Group | He D.,University of Illinois at Chicago | Bechhoefer E.,Green Power Monitoring Systems LLC
Journal of Failure Analysis and Prevention | Year: 2014

The diagnosis of bearing health through the quantification of accelerometer data has been an area of interest for many years and has resulted in numerous signal processing methods and algorithms. This paper proposes a new diagnostic approach that combines envelope analysis, time synchronous resampling, and spectral averaging of vibration signals to extract condition indicators (CIs) used for rolling-element bearing fault diagnosis. First, the accelerometer signal is digitized simultaneously with tachometer signal acquisition. Then, the digitized vibration signal is band pass filtered to retain the information associated with the bearing defects. Finally, the tachometer signal is used to time synchronously resample the vibration data which allows the computation of a spectral average and the extraction of the CIs used for bearing fault diagnosis. The proposed technique is validated using the vibration output of seeded fault steel bearings on a bearing test rig. The result is an effective approach validated to diagnose all four bearing fault types: inner race, outer race, ball, and cage. © 2014 ASM International. Source

Zhao F.,University of Alberta | Tian Z.,University of Alberta | Bechhoefer E.,Green Power Monitoring Systems LLC | Zeng Y.,Concordia University at Montreal
IEEE Transactions on Reliability | Year: 2015

In this paper, we develop an integrated prognostics method considering a time-varying operating condition, which integrates physical gear models and sensor data. By taking advantage of stress analysis in finite element modeling (FEM), the degradation process governed by Paris' law can adjust itself immediately to respond to the changes of the operating condition. The capability to directly relate the load to the damage propagation is a key advantage of the proposed integrated prognostics approach over the existing data-driven methods for dealing with time-varying operating conditions. In the proposed method, uncertainties in material parameters are considered as sources responsible for randomness in the predicted failure life. The joint distribution of material parameters is updated as sensor data become available. The updated distribution better characterizes the material parameters, and reduces the uncertainty in life prediction for the specific individual unit under condition monitoring. The update process is realized via Bayesian inference. To reduce the computational effort, a polynomial chaos expansion (PCE) collocation method is applied in computing the likelihood function in the Bayesian inference and the predicted failure time distribution. Examples based on crack propagation in a spur gear tooth are given to demonstrate the effectiveness of the proposed method. In addition, the example also shows that the proposed approach is effective even when the current loading profile is different from the loading profile under which historical data were collected. © 1963-2012 IEEE. Source

Bechhoefer E.,Green Power Monitoring Systems LLC | Fang A.,Comell University | Garcia E.,Comell University
Annual Forum Proceedings - AHS International | Year: 2014

Vibration derived from the main rotor dynamics and imbalance causes premature wear to the aircraft components, and can cause pilot fatigue. While improvements have been made in rotor track and balance (RTB) techniques; there is room to enhance the quality of the recommended RTB adjustments. One aspect that limits the development of RTB algorithms is the difficulty in quantifying the performance of new algorithms. This is because there are limited data sets to work on, and no agreed upon metrics on which to measure RTB performance. This paper develops a methodology to simulate the vibration due to injecting a fault into the rotor system, and demonstrates metrics to evaluate the performance of a RTB algorithm. A new Bayes RTB method is evaluated against a standard least squares technique. In addition, an Expert System technique is presented to automate the selection of active adjustments. Copyright© 2014 by the Amencan Helicopter Society International, Inc. All rights reserved. Source

Qu Y.,University of Illinois at Chicago | Van Hecke B.,University of Illinois at Chicago | He D.,University of Illinois at Chicago | Yoon J.,University of Illinois at Chicago | And 2 more authors.
MFPT 2014 Conference: Technology Solutions for Affordable Sustainment | Year: 2014

In recent years, acoustic emission (AE) sensors and AE based techniques have been developed and tested for gearbox fault diagnosis. In general, AE based techniques require much higher sampling rate than vibration analysis based techniques for gearbox fault diagnosis. Therefore, it is questionable if an AE based technique would give a better or at least the same performance as the vibration analysis based techniques using the same sampling rate. To answer the question, this paper presents a comparative study for gearbox tooth damage level diagnostics using AE and vibration measurements, the first known attempt to compare the gearbox fault diagnostic performance of AE and vibration analysis based approaches using the same sampling rate. Partial tooth cut faults are seeded in a gearbox test rig and experimentally tested. Results have shown that AE based approach has the potential to differentiate gear tooth damage levels in comparison with vibration based approach. While vibration signals are easily affected by mechanical resonance, the AE signals show more stable performance. Source

Saidi L.,University of Tunis | Ben Ali J.,University of Tunis | Benbouzid M.,French National Center for Scientific Research | Benbouzid M.,Shanghai Maritime University | Bechhoefer E.,Green Power Monitoring Systems LLC
ISA Transactions | Year: 2016

A critical work of bearing fault diagnosis is locating the optimum frequency band that contains faulty bearing signal, which is usually buried in the noise background. Now, envelope analysis is commonly used to obtain the bearing defect harmonics from the envelope signal spectrum analysis and has shown fine results in identifying incipient failures occurring in the different parts of a bearing. However, the main step in implementing envelope analysis is to determine a frequency band that contains faulty bearing signal component with the highest signal noise level. Conventionally, the choice of the band is made by manual spectrum comparison via identifying the resonance frequency where the largest change occurred. In this paper, we present a squared envelope based spectral kurtosis method to determine optimum envelope analysis parameters including the filtering band and center frequency through a short time Fourier transform. We have verified the potential of the spectral kurtosis diagnostic strategy in performance improvements for single-defect diagnosis using real laboratory-collected vibration data sets. © 2016 ISA. Source

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