A Robust Bearing Fault Detection and Diagnosis Technique for Brushless DC Motors Under Non-stationary Operating Conditions

Abed, Wathiq; Sharma, Sanjay; Sutton, Robert I.; Motwani, Amit

doi:10.1007/s40313-015-0173-7

Cited by 56 publications

(39 citation statements)

References 37 publications

(36 reference statements)

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“…Figure 4 shows the distribution of the papers in relation to the type of failure discussed. The results are in accordance with the failure distribution presented in various papers [7][8][9][10], and, in turn, this means that the research efforts are consistent with the most common diffuse failures.…”

Section: Discussionsupporting

confidence: 90%

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Failure Detection by Signal Similarity Measurement of Brushless DC Motors

Fico¹,

Vázquez²,

Prats

et al. 2019

Energies

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In recent years, Brushless DC (BLDC) motors have been gaining popularity as a solution for providing mechanical power, starting from low cost mobility solutions like the electric bikes, to high performance and high reliability aeronautical Electro-Mechanical Actuator (EMA). In this framework, the availability of fault detection tools suited to these types of machines appears necessary. There is already a vast literature on this topic, but only a small percentage of the proposed techniques have been developed to a sufficiently high Technology Readiness Level (TRL) to be implementable in industrial applications. The investigation on the state of the art carried out during the first phase of the present work, tried to collect the techniques which are closest to possible implementation. To fill a gap identified in the current techniques, a partial demagnetisation detection method is proposed in this paper. This technique takes advantage of the asymmetries generated in the current by the missing magnetic flux to detect the failure. Simulations and laboratory experiments have been carried out to validate the idea, showing the potential and the easy implementation of the method. The results have been examined in detail and satisfactory conclusions have been drawn.

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Section: Discussionsupporting

confidence: 90%

“…Only a portion of the selected papers ( [8,9,[11][12][13][14][15][16][17][18]) clearly show statistics on the fault detection rates and mostly are based on the use of AI.…”

Section: Best Detection Resultsmentioning

confidence: 99%

Failure Detection by Signal Similarity Measurement of Brushless DC Motors

Fico¹,

Vázquez²,

Prats

et al. 2019

Energies

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show abstract

“…Figure 6 shows the distribution of the papers in relation to the type of failure discussed. The results are in accordance with the failure distribution presented in various papers [29,31,36,54], and, in turn, this means that the research efforts are consistent with the failures occurrence. Due to the intense research in this field, many of the motor parameters have been used for fault detection purposes.…”

Section: Discussionsupporting

confidence: 90%

“…Only some selected papers ( [10,11,18,19,26,[29][30][31]35,43,45]) provide statistics on the rate of error detection and are mostly based on the use of AI. It is very complex to compare all these results, since the test conditions are not uniform.…”

Section: Rq6: Best Detection Resultsmentioning

confidence: 99%

High Technology Readiness Level Techniques for Brushless Direct Current Motors Failures Detection: A Systematic Review

Fico¹,

Prats

Ierardi

2020

Energies

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Many papers related to this topic can be found in the bibliography; however, just a modest percentage of the introduced techniques are developed to a Technology Readiness Level (TRL) sufficiently high to be implementable in industrial applications. This paper is focused precisely on the review of this specific topic. The investigation on the state of the art has been carried out as a systematic review, a very rigorous and reliable standardised scientific methodology, and tries to collect the articles which are closer to a possible implementation. This selection has been carefully done with the definition of a series of rules, drawn to represent the adequate level of readiness of fault detection techniques which the various articles propose.

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“…Main contributions of this paper are as follows: Fast and very detailed results with advanced and sensitive vibration sensors combined with a data-gathering device with a high sampling rate and cloud technologies used, compared to other research that measured vibrations, for example [15][16][17].…”

Section: Introductionmentioning

confidence: 99%

Application of Advanced Vibration Monitoring Systems and Long Short-Term Memory Networks for Brushless DC Motor Stator Fault Monitoring and Classification

et al. 2020

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In this research, electric motors faults and their identification is reviewed. Brushless direct-current (BLDC) motors stator fault identification using long short-term memory neural networks were analyzed. A proposed method of vibration data acquisition using cloud technologies with high accuracy, feature extraction using spectral entropy, and instantaneous frequency and standardization using mean and standard deviation was reviewed. Additionally, model training with raw and standardized data was compared. A total model accuracy of 97.10 percent was achieved. The proposed methods could successfully identify the motor stator status from normal, to loss of stator winding imminent and arcing, and lastly to open circuit in stator winding—motor needing to stop immediately—by using gathered data from real experiments, training the model and testing it theoretically.

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A Robust Bearing Fault Detection and Diagnosis Technique for Brushless DC Motors Under Non-stationary Operating Conditions

Cited by 56 publications

References 37 publications

Failure Detection by Signal Similarity Measurement of Brushless DC Motors

Failure Detection by Signal Similarity Measurement of Brushless DC Motors

High Technology Readiness Level Techniques for Brushless Direct Current Motors Failures Detection: A Systematic Review

Application of Advanced Vibration Monitoring Systems and Long Short-Term Memory Networks for Brushless DC Motor Stator Fault Monitoring and Classification

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