A fault diagnosis scheme of rolling element bearing based on near-field acoustic holography and gray level co-occurrence matrix

Lu, Wenbo; Jiang, Weikang; Wu, Haijun; Hou, Junjian

doi:10.1016/j.jsv.2012.03.008

Cited by 55 publications

(32 citation statements)

References 37 publications

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“…The acoustic signals used in Wang and He, 8 Lu et al, 9 and He et al 10 are notably different from the acoustic emission (AE) signals used in this article. The acoustic signals used in Wang and He, 8 Lu et al 9 and He et al 10 were all acquired through one or more microphones, whereas the AE signals acquired in this article were collected using a wideband (WD) AE sensor.…”

Section: Introductionmentioning

confidence: 91%

“…Another recent study presented an acoustic-based diagnostic technique that was constructed from near-field acoustic holography and the gray-level co-occurrence matrix. 9 Using a fast Fourier transform (FFT)-based near-field acoustic holography, the sound fields and their corresponding acoustic images for different conditions were obtained. Then, after feature extraction, the support vector machines algorithm was utilized to diagnose various types of rolling element faults.…”

Section: Introductionmentioning

confidence: 99%

“…The acoustic signals used in Wang and He, 8 Lu et al 9 and He et al 10 were all acquired through one or more microphones, whereas the AE signals acquired in this article were collected using a wideband (WD) AE sensor. AE sensors are generally piezoelectric devices designed to provide a voltage output when exposed to an ultrasonic motion at the surface to which they are coupled, usually generated from an elastic stress wave resulting from a defect or unwanted mechanism within the structure.…”

Section: Introductionmentioning

confidence: 99%

“…AE sensors are generally piezoelectric devices designed to provide a voltage output when exposed to an ultrasonic motion at the surface to which they are coupled, usually generated from an elastic stress wave resulting from a defect or unwanted mechanism within the structure. 11 Thus, the information collected using an AE sensor is quite different than the microphones used in Wang and He, 8 Lu et al, 9 and He et al, 10 which collect the signal information generated from the sound field produced by the object of interest. A comparative study on scratch detection of a self-aligning spherical journal bearing using microphone, as well as vibration and AE sensors was reported in Raharjo et al 12 The study concluded that although all three sensors could be used to detect a bearing scratch, the AE sensor was the most sensitive one among the three tested sensors.…”

Section: Introductionmentioning

confidence: 99%

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Bearing fault diagnosis based on a new acoustic emission sensor technique

Hecke

Qu²,

2014

Proceedings of the Institution of Mechanical Engineers, Part O:

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The diagnosis of bearing health by quantifying acoustic emission data has been an area of interest for recent years due to the numerous advantages over vibration-based techniques. However, most acoustic emission-based methodologies to date are data-driven technologies. This research takes a novel approach combining a heterodyne-based frequency reduction technique, time synchronous resampling, and spectral averaging to process acoustic emission signals and extract condition indicators for bearing fault diagnosis. The heterodyne technique allows the acoustic emission signal frequency to be shifted from several megahertz to less than 50 kHz, which is comparable to that of vibration-based techniques. Then, the digitized 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 acoustic emission data, allowing the computation of a spectral average which in turn enables the extraction and evaluation of condition indicators for bearing fault diagnosis. The presented technique is validated using the acoustic emission signals of seeded fault steel bearings on a bearing test rig. The result is an effective acoustic emission-based approach validated to diagnose all four fault types: inner race, outer race, ball, and cage.

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Section: Introductionmentioning

confidence: 91%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Bearing fault diagnosis based on a new acoustic emission sensor technique

Hecke

Qu²,

2014

Proceedings of the Institution of Mechanical Engineers, Part O:

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“…At every stage, the decision is made from wireless sensor networks point of view to save battery energy. Here, along with good condition of the bearings, three fault conditions (inner race fault, outer race fault, inner and out race fault) are also considered [19]. With this set up, one can build the best possible wireless sensor network in an industrial environment.…”

Section: Introductionmentioning

confidence: 99%

Lifetime Maximization of Wireless Sensor Networks for a Fault Diagnosis System Using LEACH Protocol

2018

Teh. vjesn.

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The breakdown in a machine created by a fault will greatly affect the plant operation. The frame work of fault diagnosis of machines using machine learning techniques is an established area. Here, the fault diagnosis system is implemented with the help of wireless sensor networks (WSN). Each machine in the plant is fitted with sensors (node) from which the fault diagnosis is carried out. The signals/messages from each node are transmitted to a base station, which acts as a central control unit for the entire plant. Fault diagnosis using WSN in a factory setup has few challenges. The major issue in WSN is the life time of the nodes, as they are situated far away from base station in many plants like thermal power plant, refinery, and petroleum industries. To address the life time of the nodes many researchers have developed many protocols like LEACH, SEP, ERP, HCR, HEED, and PEGASIS. The plants need customisation in terms of choosing suitable algorithms and choosing location of the base station within the plant for better life time of the nodes. This paper presents results of both experimental and simulation studies of a typical plant, where the vibration signals from each machine are acquired and through machine learning techniques the fault diagnosis is performed with the help of wireless sensor networks. For illustrative purpose, a well reported bearing fault diagnosis data set is taken up and fault diagnosis case study was performed from wireless sensor networks point of view (experimental study). Here, at every stage, the computational time is taken as a primary concern which affects the life time of the sensor nodes. Then, the WSN of 18 sensor nodes representing 18 machines with LEACH protocol is simulated in Matlab© to study the life time characteristics of each node while keeping base station at different locations. The life time of different nodes is heavily dependent on the location of the base station. Finding the right location of the base station for a given plant is another contribution of this work.

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Methods and Tools for the Operational Reliability Optimisation of Large-Scale Industrial Wind Turbines

González-Carrato

Márquez²,

Alexander³

et al. 2015

Advances in Intelligent Systems and Computing

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Wind turbines (WT) maintenance management is in continuous development to improve the reliability, availability, maintainability and safety (RAMS) of WTs, and to achieve time and cost reductions. The optimisation of the operation reliability involves the supervisory control and data acquisition to guarantee correct levels of RAMS. A fault detection and diagnosis methodology is proposed for large-scale industrial WTs. The method applies the wavelet and Fourier analysis to vibration signals. A number of turbines (up to 3) of the same type will be instrumented in the same wind farm. The data collected from the individual turbines will be fused and analysed together in order to determine the overall reliability of this particular wind farm and wind turbine type. It is expected that data fusion will allow a significant improvement in overall reliability since the value of the information gained from the various condition monitoring systems will be enhanced. Effort will also focus on the successful application of dependable embedded computer systems for the reliable implementation of wind turbine condition monitoring and control technologies.

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A fault diagnosis scheme of rolling element bearing based on near-field acoustic holography and gray level co-occurrence matrix

Cited by 55 publications

References 37 publications

Bearing fault diagnosis based on a new acoustic emission sensor technique

Bearing fault diagnosis based on a new acoustic emission sensor technique

Lifetime Maximization of Wireless Sensor Networks for a Fault Diagnosis System Using LEACH Protocol

Methods and Tools for the Operational Reliability Optimisation of Large-Scale Industrial Wind Turbines

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