Fault diagnosis of low speed bearing based on acoustic emission signal and multi-class relevance vector machine

Widodo, Achmad; Yang, Bo‐Suk; Kim, Eric Y.; Tan, Andy; Mathew, Joseph

doi:10.1080/10589750802378974

Cited by 50 publications

(21 citation statements)

References 18 publications

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“…According to published results, AE sensor was more sensitive than vibration sensor in monitoring low speed bearings. The use of AE sensor, along with intelligent technique, that is, relevance vector machine (RVM), for help in machine diagnostics process using such sensor was reported by Widodo et al [11]. Another research in this area was reported by El-Ghamry et al [12] in which he used the automated pattern-recognition procedure to perform fault diagnostics of reciprocating machines by AE sensor.…”

Section: International Journal Of Rotating Machinerymentioning

confidence: 97%

Confirmation of Thermal Images and Vibration Signals for Intelligent Machine Fault Diagnostics

Widodo

Satrijo

Prahasto

et al. 2012

International Journal of Rotating Machinery

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This paper deals with the maintenance technique for industrial machinery using the artificial neural network so-called selforganizing map (SOM). The aim of this work is to develop intelligent maintenance system for machinery based on an alternative way, namely, thermal images instead of vibration signals. SOM is selected due to its simplicity and is categorized as an unsupervised algorithm. Following the SOM training, machine fault diagnostics is performed by using the pattern recognition technique of machine conditions. The data used in this work are thermal images and vibration signals, which were acquired from machine fault simulator (MFS). It is a reliable tool and is able to simulate several conditions of faulty machine such as unbalance, misalignment, looseness, and rolling element bearing faults (outer race, inner race, ball, and cage defects). Data acquisition were conducted simultaneously by infrared thermography camera and vibration sensors installed in the MFS. The experimental data are presented as thermal image and vibration signal in the time domain. Feature extraction was carried out to obtain salient features sensitive to machine conditions from thermal images and vibration signals. These features are then used to train the SOM for intelligent machine diagnostics process. The results show that SOM can perform intelligent fault diagnostics with plausible accuracies.

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Section: International Journal Of Rotating Machinerymentioning

confidence: 97%

Confirmation of Thermal Images and Vibration Signals for Intelligent Machine Fault Diagnostics

Widodo

Satrijo

Prahasto

et al. 2012

International Journal of Rotating Machinery

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“…19 The high dimension of feature vector tends to increase computational complexity and computation time, which is not desirable for a practical diagnosis system. Meanwhile, it will cause the training of classifier to be very difficult.…”

Section: Feature Extractionmentioning

confidence: 99%

Fault diagnosis and classification of water pump using adaptive neuro-fuzzy inference system based on vibration signals

Moosavian

Khazaee

Ahmadi

et al. 2015

Structural Health Monitoring

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This article presents a procedure to intelligent fault diagnosis of water pump using vibration monitoring and adaptive neuro-fuzzy inference system. Vibration data from a water pump with three different conditions, namely, healthy, looseness, and misalignment faults, were acquired using an accelerometer sensor. On this basis, the proposed method extracted different feature parameters from the vibration signals in the frequency-domain to establish feature vectors. Finally, these features were input into adaptive neuro-fuzzy inference system to carry out faulty pattern classification. The performance of the system was validated by applying the testing data set to the trained adaptive neuro-fuzzy inference system model. The experimental results showed that this method had a high classification accuracy for both training and testing data sets. Therefore, the proposed approach can reliably recognize different fault categories and serve as an intelligent fault diagnosis system in real applications.

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“…The authors remarked the potential of AE to detect defects in low-speed bearings. Widodo et al 23 applied multi-class relevance vector machine for defect detection in low-speed bearings. This study was aimed at finding a reliable method for low-speed machines fault diagnosis based on AE signal.…”

Section: Introductionmentioning

confidence: 99%

An experimental study on the applicability of acoustic emission for wind turbine gearbox health diagnosis

Chacon

Artigao

Kappatos

et al. 2016

Journal of Low Frequency Noise, Vibration and Active Control

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Condition monitoring of wind turbine gearboxes has mainly relied upon vibration, oil analysis and temperature monitoring. However, these techniques are not well suited for detecting early stage damage. Acoustic emission is gaining ground as a complementary condition monitoring technique as it offers earlier fault detection capability compared with other more established techniques. The objective of early fault detection in wind turbine gearboxes is to avoid unexpected catastrophic breakdowns, thereby reducing maintenance costs and increase safety. The aim of this investigation is to present an experimental study the impact of operational conditions (load and torque) in the acoustic emission activity generated within the wind turbine gearbox. The acoustic emission signature for a healthy wind turbine gearbox was obtained as a function of torque and power output, for the full range of operational conditions. Envelope analysis was applied to the acoustic emission signals to investigate repetitive patterns and correlate them with specific gearbox components. The analysis methodology presented herewith can be used for the reliable assessment of wind turbine gearbox subcomponents using acoustic emission.

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Fault diagnosis of low speed bearing based on acoustic emission signal and multi-class relevance vector machine

Cited by 50 publications

References 18 publications

Confirmation of Thermal Images and Vibration Signals for Intelligent Machine Fault Diagnostics

Confirmation of Thermal Images and Vibration Signals for Intelligent Machine Fault Diagnostics

Fault diagnosis and classification of water pump using adaptive neuro-fuzzy inference system based on vibration signals

An experimental study on the applicability of acoustic emission for wind turbine gearbox health diagnosis

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