Early fault diagnosis of bearing and stator faults of the single-phase induction motor using acoustic signals

Głowacz, Adam; Głowacz, W.; Głowacz, Z.; Kozik, J.

doi:10.1016/j.measurement.2017.08.036

Cited by 315 publications

(159 citation statements)

References 54 publications

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“…In the literature, different types of signals have been used as an indicator for motor fault diagnosis. These indicators include magnetic flux, vibration, stator current (negative and zero sequence or third harmonic component), zero sequence component of voltage, thermal image processing, instantaneous real power and reactive power, phase shift between stator and supply voltages, and acoustic noise [9,11,[15][16][17][18][19][20][21][22][23][24][25][26][27][28]. Moreover, many researchers used the instantaneous torque as a fault indicator in induction motors [18,21,[29][30][31].…”

Section: Introductionmentioning

confidence: 99%

An Efficient Stator Inter-Turn Fault Diagnosis Tool for Induction Motors

2018

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Induction motors constitute the largest proportion of motors in industry. This type of motor experiences different types of failures, such as broken bars, eccentricity, and inter-turn failure. Stator winding faults account for approximately 36% of these failures. As such, condition monitoring is used to protect motors from sudden breakdowns. This paper proposes the use of neural networks as an efficient diagnostic tool for estimating the percentage of stator winding shorted turns in three-phase induction motors. A MATLAB-based model was developed and simulated under different fault-load combination cases for different sizes of motors. The motor's developed electromechanical torque was selected as a fault indicator. For the design and training of the neural network, the mean, variance, max, min, and F120 time based on statistical and frequency-related features were found to be very distinct for correlating the captured electromechanical torque with its corresponding percentage of shorted turns. In the training phase of the neural network, five different motors were used and are referred to as seen motors. On the other hand, for testing the efficiency of the developed diagnostic tool, the electromechanical torque under different fault-load combination cases, previously never seen from the first five motors and those of two new motors (referred to as unseen), was used. Testing results revealed accuracy in the range of 88-99%.

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Section: Introductionmentioning

confidence: 99%

An Efficient Stator Inter-Turn Fault Diagnosis Tool for Induction Motors

2018

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“…Some researchers reported that specific activity, namely eating, excavation and head-banging would generate the particular acoustic signal [10,11]. Over the last decade, the application of microphones has been successful to detect insects within wood [12][13][14] and to solve various engineering problems [15][16][17][18][19]. Therefore, in this study, we used the microphone electret sensor to sense the acoustic signal produced by termites.…”

Section: Introductionmentioning

confidence: 99%

A Comparison Study of Kernel Functions in the Support Vector Machine and Its Application for Termite Detection

et al. 2018

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Abstract:Termites are the most destructive pests and their attacks significantly impact the quality of wooden buildings. Due to their cryptic behavior, it is rarely apparent from visual observation that a termite infestation is active and that wood damage is occurring. Based on the phenomenon of acoustic signals generated by termites when attacking wood, we proposed a practical framework to detect termites nondestructively, i.e., by using the acoustic signals extraction. This method has the pros to maintain the quality of wood products and prevent higher termite attacks. In this work, we inserted 220 subterranean termites into a pine wood for feeding activity and monitored its acoustic signal. The two acoustic features (i.e., energy and entropy) derived from the time domain were used for this study's analysis. Furthermore, the support vector machine (SVM) algorithm with different kernel functions (i.e., linear, radial basis function, sigmoid and polynomial) were employed to recognize the termites' acoustic signal. In addition, the area under a receiver operating characteristic curve (AUC) was also adopted to analyze and improve the performance results. Based on the numerical analysis, the SVM with polynomial kernel function achieves the best classification accuracy of 0.9188.

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“…In the literature, many state of the art techniques that based on acoustic signals have been applied in many applications [1]. Recently, acoustic signals are used in diagnostic techniques of machines in the field of industry and engineering, where many rotating machines such as electric motors are used.…”

Section: Introductionmentioning

confidence: 99%

“…Recently, acoustic signals are used in diagnostic techniques of machines in the field of industry and engineering, where many rotating machines such as electric motors are used. Diagnosis of such motors is considered as a normal maintenance process [1]. Using acoustic signals is nowadays an up-to-date method for many applications of fault diagnosis and localisation in rotating machines.…”

Section: Introductionmentioning

confidence: 99%

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Mitigating Wind Induced Noise in Outdoor Microphone Signals Using a Singular Spectral Subspace Method

Eldwaik

2018

Technologies

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Abstract:Wind induced noise is one of the major concerns of outdoor acoustic signal acquisition. It affects many field measurement and audio recording scenarios. Filtering such noise is known to be difficult due to its broadband and time varying nature. In this paper, a new method to mitigate wind induced noise in microphone signals is developed. Instead of applying filtering techniques, wind induced noise is statistically separated from wanted signals in a singular spectral subspace. The paper is presented in the context of handling microphone signals acquired outdoor for acoustic sensing and environmental noise monitoring or soundscapes sampling. The method includes two complementary stages, namely decomposition and reconstruction. The first stage decomposes mixed signals in eigen-subspaces, selects and groups the principal components according to their contributions to wind noise and wanted signals in the singular spectrum domain. The second stage reconstructs the signals in the time domain, resulting in the separation of wind noise and wanted signals. Results show that microphone wind noise is separable in the singular spectrum domain evidenced by the weighted correlation. The new method might be generalized to other outdoor sound acquisition applications.

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Early fault diagnosis of bearing and stator faults of the single-phase induction motor using acoustic signals

Cited by 315 publications

References 54 publications

An Efficient Stator Inter-Turn Fault Diagnosis Tool for Induction Motors

An Efficient Stator Inter-Turn Fault Diagnosis Tool for Induction Motors

A Comparison Study of Kernel Functions in the Support Vector Machine and Its Application for Termite Detection

Mitigating Wind Induced Noise in Outdoor Microphone Signals Using a Singular Spectral Subspace Method

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