Effectiveness of Adaptive Filter Algorithms and Spectral Kurtosis in Bearing Faults Detection in a Gearbox

2014 ◽  
pp. 219-229 ◽  
Author(s):  
Faris Elasha ◽  
David Mba ◽  
Cristobal Ruiz-Carcel
Keyword(s):  
Adaptive Filter ◽  
Bearing Faults ◽  
Spectral Kurtosis ◽  
Filter Algorithms

Bearing Natural Degradation Detection in a Gearbox: A Comparative Study of the Effectiveness of Adaptive Filter Algorithms and Spectral Kurtosis

2014 ◽  
Author(s):  
Faris Elasha ◽  
Cristobal Ruiz-Carcel ◽  
David Mba
Keyword(s):  
Adaptive Filter ◽  
Adaptive Filters ◽  
Vibration Signal ◽  
Lms Algorithm ◽  
Complex Signal ◽  
Least Mean Square ◽  
Bearing Faults ◽  
Spectral Kurtosis ◽  
Bearing Defects ◽  
Filter Algorithms

Bearing faults detection at the earliest stages is vital in avoiding future catastrophic failures. Many traditional techniques have been established and utilized in detecting bearing faults, though, these diagnostic techniques are not always successful when the bearing faults take place in gearboxes where the vibration signal is complex; under such circumstances it may be necessary to separate the bearing signal from the complex signal. The objective of this paper is to assess the effectiveness of an adaptive filter algorithms compared to a Spectral Kurtosis (SK) algorithm in diagnosing a bearing defects in a gearbox. Two adaptive filters have been used for the purpose of bearing signal separation, these algorithms were Least Mean Square (LMS) and Fast Block LMS (FBLMS) algorithms. These algorithms were applied to identify a bearing defects in a gearbox employed for an aircraft control system for which endurance tests were performed. The results show that the LMS algorithm is capable of detecting the bearing fault earlier in comparison to the other algorithms.

scholarly journals Planetary bearing defect detection in a commercial helicopter main gearbox with vibration and acoustic emission

2018 ◽  
Vol 17 (5) ◽  
pp. 1192-1212 ◽  
Author(s):  
Faris Elasha ◽  
Matthew Greaves ◽  
David Mba
Keyword(s):  
Acoustic Emission ◽  
Signal To Noise Ratio ◽  
Acoustic Emissions ◽  
Research Programme ◽  
Vibration Signal ◽  
Operating Conditions ◽  
Broadband Wireless ◽  
Bearing Faults ◽  
Spectral Kurtosis ◽  
Signal Suppression

Helicopter gearboxes significantly differ from other transmission types and exhibit unique behaviours that reduce the effectiveness of traditional fault diagnostics methods. In addition, due to lack of redundancy, helicopter transmission failure can lead to catastrophic accidents. Bearing faults in helicopter gearboxes are difficult to discriminate due to the low signal-to-noise ratio in the presence of gear vibration. In addition, the vibration response from the planet gear bearings must be transmitted via a time-varying path through the ring gear to externally mounted accelerometers, which cause yet further bearing vibration signal suppression. This research programme has resulted in the successful proof of concept of a broadband wireless transmission sensor that incorporates power scavenging while operating within a helicopter gearbox. In addition, this article investigates the application of signal separation techniques in detection of bearing faults within the epicyclic module of a large helicopter (CS-29) main gearbox using vibration and acoustic emissions. It compares their effectiveness for various operating conditions. Three signal processing techniques, including an adaptive filter, spectral kurtosis and envelope analysis, were combined for this investigation. In addition, this research discusses the feasibility of using acoustic emission for helicopter gearbox monitoring.

scholarly journals A Proportionate Normalized Maximum Correntropy Criterion Algorithm with Correntropy Induced Metric Constraint for Identifying Sparse Systems

Symmetry ◽  
2018 ◽  
Vol 10 (12) ◽  
pp. 683 ◽  
Author(s):  
Yingsong Li ◽  
Yanyan Wang ◽  
Laijun Sun
Keyword(s):  
Impulse Response ◽  
Adaptive Filter ◽  
Finite Impulse Response ◽  
Least Mean Square ◽  
Sparse Systems ◽  
Simulation Results ◽  
Maximum Correntropy Criterion ◽  
Non Gaussian ◽  
Fir Systems ◽  
Filter Algorithms

A proportionate-type normalized maximum correntropy criterion (PNMCC) with a correntropy induced metric (CIM) zero attraction terms is presented, whose performance is also discussed for identifying sparse systems. The proposed sparse algorithms utilize the advantage of proportionate schemed adaptive filter, maximum correntropy criterion (MCC) algorithm, and zero attraction theory. The CIM scheme is incorporated into the basic MCC to further utilize the sparsity of inherent sparse systems, resulting in the name of the CIM-PNMCC algorithm. The derivation of the CIM-PNMCC is given. The proposed algorithms are used for evaluating the sparse systems in a non-Gaussian environment and the simulation results show that the expanded normalized maximum correntropy criterion (NMCC) adaptive filter algorithms achieve better performance than those of the squared proportionate algorithms such as proportionate normalized least mean square (PNLMS) algorithm. The proposed algorithm can be used for estimating finite impulse response (FIR) systems with symmetric impulse response to prevent the phase distortion in communication system.

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