Fault detection of broken rotor bar in LS-PMSM using random forests

Quiroz, Juan C.; Mariun, Norman; Mehrjou, Mohammad Rezazadeh; Izadi, Mahdi; Misron, Norhisam; Radzi, Mohd Amran Mohd

doi:10.1016/j.measurement.2017.11.004

Cited by 116 publications

(60 citation statements)

References 38 publications

(95 reference statements)

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“…The developed model uses a very wide population of pre-treatments and statistic tests on the data and has the ability to select good combinations of tests with higher than 85% pre-identification. Quiroz, J.C. et al [66] proposed a new approach to diagnose broken rotor bar failure in a Line Start-Permanent Magnet Synchronous Motor (LS-PMSM) using RF. The transient current signal during the motor startup was acquired from a healthy motor and a faulty motor with a broken rotor bar fault.…”

Section: Random Forest (Rf)mentioning

confidence: 99%

Machine Learning in Predictive Maintenance towards Sustainable Smart Manufacturing in Industry 4.0

et al. 2020

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Recently, with the emergence of Industry 4.0 (I4.0), smart systems, machine learning (ML) within artificial intelligence (AI), predictive maintenance (PdM) approaches have been extensively applied in industries for handling the health status of industrial equipment. Due to digital transformation towards I4.0, information techniques, computerized control, and communication networks, it is possible to collect massive amounts of operational and processes conditions data generated form several pieces of equipment and harvest data for making an automated fault detection and diagnosis with the aim to minimize downtime and increase utilization rate of the components and increase their remaining useful lives. PdM is inevitable for sustainable smart manufacturing in I4.0. Machine learning (ML) techniques have emerged as a promising tool in PdM applications for smart manufacturing in I4.0, thus it has increased attraction of authors during recent years. This paper aims to provide a comprehensive review of the recent advancements of ML techniques widely applied to PdM for smart manufacturing in I4.0 by classifying the research according to the ML algorithms, ML category, machinery, and equipment used, device used in data acquisition, classification of data, size and type, and highlight the key contributions of the researchers, and thus offers guidelines and foundation for further research.

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Section: Random Forest (Rf)mentioning

confidence: 99%

Machine Learning in Predictive Maintenance towards Sustainable Smart Manufacturing in Industry 4.0

et al. 2020

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“…Machine learning methods can be considered advanced technology with great potential for data analysis and has been successfully applied in various areas such as fault detection [ 43 ], quality prediction [ 14 , 44 ], defect classification [ 45 ], and visual inspection [ 46 ]. In the case of fault prediction, machine learning algorithms such as Random Forest are highly effective in detecting abnormal events in a process and thus can help avoid productivity loss [ 47 , 48 , 49 ]. However, machine learning algorithms encounter problems with outlier data, which can reduce the accuracy of the classification model.…”

Section: Introductionmentioning

confidence: 99%

Performance Analysis of IoT-Based Sensor, Big Data Processing, and Machine Learning Model for Real-Time Monitoring System in Automotive Manufacturing

Syafrudin

Alfian

Fitriyani

et al. 2018

Sensors

278

103

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With the increase in the amount of data captured during the manufacturing process, monitoring systems are becoming important factors in decision making for management. Current technologies such as Internet of Things (IoT)-based sensors can be considered a solution to provide efficient monitoring of the manufacturing process. In this study, a real-time monitoring system that utilizes IoT-based sensors, big data processing, and a hybrid prediction model is proposed. Firstly, an IoT-based sensor that collects temperature, humidity, accelerometer, and gyroscope data was developed. The characteristics of IoT-generated sensor data from the manufacturing process are: real-time, large amounts, and unstructured type. The proposed big data processing platform utilizes Apache Kafka as a message queue, Apache Storm as a real-time processing engine and MongoDB to store the sensor data from the manufacturing process. Secondly, for the proposed hybrid prediction model, Density-Based Spatial Clustering of Applications with Noise (DBSCAN)-based outlier detection and Random Forest classification were used to remove outlier sensor data and provide fault detection during the manufacturing process, respectively. The proposed model was evaluated and tested at an automotive manufacturing assembly line in Korea. The results showed that IoT-based sensors and the proposed big data processing system are sufficiently efficient to monitor the manufacturing process. Furthermore, the proposed hybrid prediction model has better fault prediction accuracy than other models given the sensor data as input. The proposed system is expected to support management by improving decision-making and will help prevent unexpected losses caused by faults during the manufacturing process.

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“…Several sensing techniques have been researched in the field of BRB fault diagnosis. For example, motor current signature analysis (MCSA) [2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17] is the mainstream technique, and the motor vibration signature analysis (MVSA) [18][19][20][21][22][23][24][25][26][27][28][29][30][31][32] technique is commonly used for diagnosing the presence of BRB failures. However, the sound measurement [32], temperature measurement [33], and magnetic field analysis [34] have been used less due to the inference of external factors.…”

Section: Introductionmentioning

confidence: 99%

An Improved Cyclic Modulation Spectral Analysis Based on the CWT and Its Application on Broken Rotor Bar Fault Diagnosis for Induction Motors

Zhen

Wang

et al. 2019

Applied Sciences

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Induction motors (IMs) are widely used in many manufacturing processes and industrial applications. The harsh work environment, long-time enduring, and overloads mean that it is subjected to broken rotor bar (BRB) faults. The vibration signal of IMs with BRB faults consists of the reliable modulation information used for fault diagnosis. Cyclostationary analysis has been found to be effective in identifying and extracting fault feature. The estimators of cyclic modulation spectrum (CMS) and fast spectral correlation (FSC) based on the short-time fourier transform (STFT) have higher cyclic frequency resolution, which has proven efficient in demodulating second order cyclostationary (CS2) signals. However, these two estimators have limitations of processing the maximum cyclic frequency αmax that is smaller than Fs/2 (Fs is the sampling frequency) according to Nyquist’s Theorem. In addition, they have lower carrier frequency resolution due to the fixed window size used in STFT. In order to resolve the initial shortcomings of the CMS and FSC methods, in this paper, we extended the analysis of CMS algorithm based on the continuous wavelet transform (CWT), which enlarged the maximum cyclic frequency range to Fs/2 and provides higher carrier frequency resolution because the CWT has the advantage of multi-resolution analysis. The reliability and applicability of the proposed method for fault components localization were validated by CS2 simulation signals. Compared to CMS and FSC methods, the proposed approach shows better performance by analyzing vibration signals between healthy motor and faulty motor with one BRB fault under 0%, 20%, 40%, and 80% load conditions.

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Fault detection of broken rotor bar in LS-PMSM using random forests

Cited by 116 publications

References 38 publications

Machine Learning in Predictive Maintenance towards Sustainable Smart Manufacturing in Industry 4.0

Machine Learning in Predictive Maintenance towards Sustainable Smart Manufacturing in Industry 4.0

Performance Analysis of IoT-Based Sensor, Big Data Processing, and Machine Learning Model for Real-Time Monitoring System in Automotive Manufacturing

An Improved Cyclic Modulation Spectral Analysis Based on the CWT and Its Application on Broken Rotor Bar Fault Diagnosis for Induction Motors

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