Faris Elasha

A Comparative Study of the Effectiveness of Adaptive Filter Algorithms, Spectral Kurtosis and Linear Prediction in Detection of a Naturally Degraded Bearing in a Gearbox

AbstractDiagnosing bearing faults at the earliest stages is critical in avoiding future catastrophic failures. Many techniques have been developed and applied in diagnosing bearings faults; however, these traditional diagnostic techniques are not always successful when the bearing fault occurs in gearboxes where the vibration response is complex; under such circumstances, it may be necessary to separate the bearing signal from the complex signal. In this paper, an adaptive filter has been applied for the purpose of bearing signal separation. Four algorithms were compared to assess their effectiveness in diagnosing a bearing defect in a gearbox, least mean square (LMS), linear prediction, spectral kurtosis and fast block LMS. These algorithms were applied to decompose the measured vibration signal into deterministic and random parts with the latter containing the bearing signal. These techniques were applied to identify a bearing fault 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 with the other algorithms.

Diagnostics of a Defective Bearing Within a Planetary Gearbox with Vibration and Acoustic Emission

Whilst vibration analysis of planetary gearbox faults is relatively well established, the application of Acoustic Emissions (AE) to this field is still in its infancy. For planetary-type gearboxes it is more challenging to diagnose bearing faults due to the dynamically changing transmission paths which contribute to masking the vibration signature of interest. The present study is aimed at developing a series of signal processing procedures to reduce the effect of background noise whilst extracting the fault feature from AE and vibration signatures. Three signal processing techniques including an adaptive filter, spectral kurtosis and envelope analysis, were applied to AE and vibration data acquired from a simplified planetary gearbox test rig with a seeded bearing defect. The results show that AE identified the defect earlier than vibration analysis irrespective of the tortuous transmission path.

Effectiveness of adaptive filter algorithms and spectral kurtosis in bearing faults detection in a gearbox

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.

Bearing signal separation enhancement with application to a helicopter transmission system

AbstractBearing vibration signal separation is essential for fault detection of gearboxes, especially where the vibration is nonstationary, susceptible to background noise, and subjected to an ardu...

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

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.Copyright

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

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.

Helicopter gearbox bearing fault detection using separation techniques and envelope analysis

The main gearbox (MGB) is a crucial part of a helicopter. MGB bearings suffer intensively from stress and friction during flights hence concerns for their health condition and detecting potential defects become critical for the sake of operation safety and system reliability. In this study, bearing defects were seeded in the second epicyclic stage bearing of a commercial Class A helicopter MGB. Vibration and tachometer signals were recorded simultaneously for the purpose of fault diagnosis. The tests were carried out at different power and speed conditions for various seeded bearing defects. This paper presents a comparison of signal processing techniques employed to identify the presence of the defects masked by strong background noise generated from an operation helicopter MGB.

Vibration and Acoustics Emissions Analysis of Helicopter Gearbox, A Comprative Study

This paper 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 (AE). It compares their effectiveness for various operating conditions. Three signal processing techniques including an adaptive filter, spectral kurtosis and envelope analysis, were investigated. In addition, this research discusses the feasibility of using AE in helicopter gearbox monitoring.

Improving condition indicators for helicopter health and usage monitoring systems

Purpose – The purpose of this paper is to suggest new method for improving the condition indicators (CIs) used in health and usage monitoring system based on signal separation of gears. Design/methodology/approach – The research method is based on employing signal separation techniques to improve gears signal and fault signature. The signal separation is based on adaptive filters concept. Findings – CIs estimated for the deterministic part of vibration signal show higher sensitivity to gears faults in comparison to indicators estimated based on the original signal. This method proposed could enhance early fault detection in gears, particularly for those applications where strong background noise from other sources in the machine masks the characteristics fault components. Originality/value – The contribution of this research is improving the CIs currently used for helicopter gearboxes. As consequence the safe operation and availability will be improved.