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.

Study and Improving on Adaptive Filter

2011 ◽  
Vol 121-126 ◽  
pp. 1392-1396
Author(s):  
Hong Xia Pan ◽  
Ying Ying Zhang
Keyword(s):  
Adaptive Filter ◽  
Vibration Signal ◽  
Lms Algorithm ◽  
Signal Denoising ◽  
Least Mean Square ◽  
Theoretical Significance ◽  
Fast Convergence Rate ◽  
Tangent Function ◽  
Gearbox Vibration ◽  
Fault Feature Extraction

In this paper the principle of adaptive filter and various least mean square (LMS) adaptive filter algorithm is studied, based on the related hyperbolic tangent function LMS algorithm is presented, referred to as CTanh-LMS algorithm. Simulation results show that, compared with other adaptive filter algorithm, this method has better denoising ability, and the algorithm is simple, fast convergence rate, and can satisfy the gearbox vibration signal denoising requirements. The proposed algorithm can not only solve the gearbox fault feature extraction, and give adaptive filter algorithm research provides a new means, has important theoretical significance and practical value.

scholarly journals System Identification using Adaptive Filters

2020 ◽  
Vol 9 (9) ◽  
pp. 409-414
Keyword(s):  
System Identification ◽  
Adaptive Filter ◽  
Adaptive Filters ◽  
Lms Algorithm ◽  
Mean Square ◽  
Least Mean Square ◽  
Iir Filter ◽  
Unknown System

Identification of system is one of the major applications of an adaptive filters, mainly Least Mean Square (LMS) algorithm, because of its ease in calculations, the ability to withstand or overcome any conditions. The unknown System can be a FIR or an IIR filter. Same input is fed into both undefined system (which is unknown to us) and the adaptive filter, their outputs will be subtracted and the error subtracted signal will be given to adaptive filter. The adaptive filter is modified until the system which is unknown and the adaptive filter becomes relatively equal. System identification defines the type and functionality of the system. By utilizing the weights, the output of the system for any input can be predicted.

scholarly journals Adaptive method to predict and track unknown system behaviors using RLS and LMS algorithms

2021 ◽  
Vol 34 (1) ◽  
pp. 133-140
Author(s):  
Teimour Tajdari
Keyword(s):  
Input Signal ◽  
Output Signal ◽  
Adaptive Filters ◽  
Adaptive Method ◽  
Lms Algorithm ◽  
Recursive Least Squares ◽  
Least Mean Square ◽  
Filter Output ◽  
System Output ◽  
Unknown System

This study investigates the ability of recursive least squares (RLS) and least mean square (LMS) adaptive filtering algorithms to predict and quickly track unknown systems. Tracking unknown system behavior is important if there are other parallel systems that must follow exactly the same behavior at the same time. The adaptive algorithm can correct the filter coefficients according to changes in unknown system parameters to minimize errors between the filter output and the system output for the same input signal. The RLS and LMS algorithms were designed and then examined separately, giving them a similar input signal that was given to the unknown system. The difference between the system output signal and the adaptive filter output signal showed the performance of each filter when identifying an unknown system. The two adaptive filters were able to track the behavior of the system, but each showed certain advantages over the other. The RLS algorithm had the advantage of faster convergence and fewer steady-state errors than the LMS algorithm, but the LMS algorithm had the advantage of less computational complexity.

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.

Variable Step Size LMS Algorithm Based on Two Cooperative Adaptive Filters

2011 ◽  
Vol 268-270 ◽  
pp. 1168-1172
Author(s):  
Qing Feng Wang ◽  
Chuan Lin
Keyword(s):  
Adaptive Filter ◽  
Adaptive Filters ◽  
Lms Algorithm ◽  
Variable Step Size ◽  
Step Size ◽  
Output Noise ◽  
Variable Step ◽  
Tracking Ability ◽  
New Algorithms ◽  
Current Stage

A new variable step size LMS algorithm (CoLMS algorithm) based on two cooperative adaptive filters was proposed. In the CoLMS algorithm, the step size of each component filter was adjusted according to the comparison result of the two component filters’ performance at current stage. And the output of the better component adaptive filter was chosen as that of the overall adaptive filter. The CoLMS algorithm is not sensitive to the magnitude of the output noise and has a good tracking ability in the stationary or slowly changed environment. In order to further improve the tracking ability of CoLMS in abruptly changed environment, a modified CoLMS algorithm is also presented. The efficiency of the new algorithms is verified by the simulation results in system identification under the noises of different magnitudes.

Extracting symptoms of bearing faults from noise using a non-linear neural filter

2002 ◽  
Vol 216 (2) ◽  
pp. 169-179 ◽  
Author(s):  
Y Shao ◽  
K Nezu
Keyword(s):  
Adaptive Filter ◽  
Signal To Noise Ratio ◽  
Effective Means ◽  
Failure Detection ◽  
Lms Algorithm ◽  
Least Mean Square ◽  
Signal To Noise ◽  
Non Linear ◽  
Detection Capabilities ◽  
Noise Ratio

Improving the signal-to-noise ratio is an important feature for the early detection of faults in bearings subject to large amounts of environmental noises. A method is proposed for improving the signal-to-noise ratio by adaptive neural filtering (ANF). A comparison of failure detection capabilities of a linear adaptive filter using the least mean square (LMS) algorithm and a non-linear adaptive filter using the ANF algorithm in conditions of large amounts of environmental noise is made. Experimental results show that an adaptive filter using a neural filtering algorithm is an effective means for extracting the symptoms of a bearing fault under such conditions.

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