Fault diagnosis of belt conveyor idlers based on gradient boosting decision tree
Maintenance planning and control should prioritize predictive techniques (e.g., vibration analysis on critical components such as belt conveyors and idlers) for addressing the low reliability of bulk transportation systems. The extensive length of the belt conveyor hampers manual inspections and mac...
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Veröffentlicht in: | International journal of advanced manufacturing technology 2024-06, Vol.132 (7-8), p.3479-3488 |
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creator | Soares, João L. L. Costa, Thiago B. Moura, Lis S. Sousa, Walter S. Mesquita, Alexandre L. A. Mesquita, André L. A. de Figueiredo, Jullyane M. S. Braga, Danilo S. |
description | Maintenance planning and control should prioritize predictive techniques (e.g., vibration analysis on critical components such as belt conveyors and idlers) for addressing the low reliability of bulk transportation systems. The extensive length of the belt conveyor hampers manual inspections and machine learning based on vibration measurements becomes an effective method for fault diagnosis. Models that classify faults, such as Gradient Boosting Decision Tree (GBDT), offer flexible algorithms that optimize decision tree classification through gradient-based techniques for minimizing predictive function errors. However, in case of non-stationary and nonlinear vibration signals, traditional techniques like Fourier Transform can hinder vibration analysis. Methods such as Wavelet Packet Decomposition (WPD) have emerged as an alternative to improve defect detection by extracting energy from signal frequency bands. This paper proposes a combination of WPD and GBDT for feature extraction and classification, respectively, for diagnosing two different failure modes in laboratory belt conveyor idlers, namely, bearing faults and surface wear. GBDT hyperparameters were well-fitted from 21 boosting stages, corroborating the high flexibility of the classification algorithm applied for less robust datasets. Furthermore, GBDT models achieved diagnosis accuracies of 100% for bearing defects and 97.5% for surface wear, showing the effectiveness of the combination for fault identification. |
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L. ; Costa, Thiago B. ; Moura, Lis S. ; Sousa, Walter S. ; Mesquita, Alexandre L. A. ; Mesquita, André L. A. ; de Figueiredo, Jullyane M. S. ; Braga, Danilo S.</creator><creatorcontrib>Soares, João L. L. ; Costa, Thiago B. ; Moura, Lis S. ; Sousa, Walter S. ; Mesquita, Alexandre L. A. ; Mesquita, André L. A. ; de Figueiredo, Jullyane M. S. ; Braga, Danilo S.</creatorcontrib><description>Maintenance planning and control should prioritize predictive techniques (e.g., vibration analysis on critical components such as belt conveyors and idlers) for addressing the low reliability of bulk transportation systems. The extensive length of the belt conveyor hampers manual inspections and machine learning based on vibration measurements becomes an effective method for fault diagnosis. Models that classify faults, such as Gradient Boosting Decision Tree (GBDT), offer flexible algorithms that optimize decision tree classification through gradient-based techniques for minimizing predictive function errors. However, in case of non-stationary and nonlinear vibration signals, traditional techniques like Fourier Transform can hinder vibration analysis. Methods such as Wavelet Packet Decomposition (WPD) have emerged as an alternative to improve defect detection by extracting energy from signal frequency bands. This paper proposes a combination of WPD and GBDT for feature extraction and classification, respectively, for diagnosing two different failure modes in laboratory belt conveyor idlers, namely, bearing faults and surface wear. GBDT hyperparameters were well-fitted from 21 boosting stages, corroborating the high flexibility of the classification algorithm applied for less robust datasets. 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The extensive length of the belt conveyor hampers manual inspections and machine learning based on vibration measurements becomes an effective method for fault diagnosis. Models that classify faults, such as Gradient Boosting Decision Tree (GBDT), offer flexible algorithms that optimize decision tree classification through gradient-based techniques for minimizing predictive function errors. However, in case of non-stationary and nonlinear vibration signals, traditional techniques like Fourier Transform can hinder vibration analysis. Methods such as Wavelet Packet Decomposition (WPD) have emerged as an alternative to improve defect detection by extracting energy from signal frequency bands. This paper proposes a combination of WPD and GBDT for feature extraction and classification, respectively, for diagnosing two different failure modes in laboratory belt conveyor idlers, namely, bearing faults and surface wear. GBDT hyperparameters were well-fitted from 21 boosting stages, corroborating the high flexibility of the classification algorithm applied for less robust datasets. Furthermore, GBDT models achieved diagnosis accuracies of 100% for bearing defects and 97.5% for surface wear, showing the effectiveness of the combination for fault identification.</description><subject>Algorithms</subject><subject>Belt conveyors</subject><subject>CAE) and Design</subject><subject>Component reliability</subject><subject>Computer-Aided Engineering (CAD</subject><subject>Critical components</subject><subject>Decision trees</subject><subject>Defects</subject><subject>Effectiveness</subject><subject>Engineering</subject><subject>Failure modes</subject><subject>Fault detection</subject><subject>Fault diagnosis</subject><subject>Feature extraction</subject><subject>Fourier transforms</subject><subject>Frequencies</subject><subject>Idlers</subject><subject>Industrial and Production Engineering</subject><subject>Machine learning</subject><subject>Mechanical Engineering</subject><subject>Media Management</subject><subject>Original Article</subject><subject>Predictive control</subject><subject>Predictive maintenance</subject><subject>Signal classification</subject><subject>System reliability</subject><subject>Transportation systems</subject><subject>Vibration analysis</subject><subject>Vibration measurement</subject><subject>Wavelet analysis</subject><subject>Wavelet transforms</subject><issn>0268-3768</issn><issn>1433-3015</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><recordid>eNp9kE1LAzEQhoMoWKt_wFPAczRfO7sepVgVCnrQc8gmkyWlbmqyFfrvjVbw5mlg5nnfgYeQS8GvBeftTeFctJxxqZlQjb5l_IjMhFaKKS6aYzLjEjqmWuhOyVkp64qDgG5GXpZ2t5moj3YYU4mFpkB7rBuXxk_cp0yj32AutLcFPU0jHbL1EceJ9imVKY4D9ehiifU0ZcRzchLspuDF75yTt-X96-KRrZ4fnhZ3K-ZkyyemZcNVawH6vvNOuwAQdONtE3rlADyi906JVjoVpACQEhsFVgfwHlB4NSdXh95tTh87LJNZp10e60ujeKMUtJp3lZIHyuVUSsZgtjm-27w3gptvc-ZgzlRz5sec4TWkDqFS4XHA_Ff9T-oLl4txqA</recordid><startdate>20240601</startdate><enddate>20240601</enddate><creator>Soares, João L. 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S.</creator><creator>Braga, Danilo S.</creator><general>Springer London</general><general>Springer Nature B.V</general><scope>AAYXX</scope><scope>CITATION</scope><orcidid>https://orcid.org/0000-0001-5605-8381</orcidid><orcidid>https://orcid.org/0000-0001-5559-5580</orcidid><orcidid>https://orcid.org/0000-0003-0260-6770</orcidid><orcidid>https://orcid.org/0009-0006-6964-7242</orcidid><orcidid>https://orcid.org/0000-0002-8092-1525</orcidid><orcidid>https://orcid.org/0009-0003-0461-3664</orcidid><orcidid>https://orcid.org/0009-0001-4326-5151</orcidid><orcidid>https://orcid.org/0009-0005-9631-1552</orcidid></search><sort><creationdate>20240601</creationdate><title>Fault diagnosis of belt conveyor idlers based on gradient boosting decision tree</title><author>Soares, João L. L. ; Costa, Thiago B. ; Moura, Lis S. ; Sousa, Walter S. ; Mesquita, Alexandre L. A. ; Mesquita, André L. A. ; de Figueiredo, Jullyane M. 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S.</au><au>Braga, Danilo S.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Fault diagnosis of belt conveyor idlers based on gradient boosting decision tree</atitle><jtitle>International journal of advanced manufacturing technology</jtitle><stitle>Int J Adv Manuf Technol</stitle><date>2024-06-01</date><risdate>2024</risdate><volume>132</volume><issue>7-8</issue><spage>3479</spage><epage>3488</epage><pages>3479-3488</pages><issn>0268-3768</issn><eissn>1433-3015</eissn><abstract>Maintenance planning and control should prioritize predictive techniques (e.g., vibration analysis on critical components such as belt conveyors and idlers) for addressing the low reliability of bulk transportation systems. The extensive length of the belt conveyor hampers manual inspections and machine learning based on vibration measurements becomes an effective method for fault diagnosis. 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subjects | Algorithms Belt conveyors CAE) and Design Component reliability Computer-Aided Engineering (CAD Critical components Decision trees Defects Effectiveness Engineering Failure modes Fault detection Fault diagnosis Feature extraction Fourier transforms Frequencies Idlers Industrial and Production Engineering Machine learning Mechanical Engineering Media Management Original Article Predictive control Predictive maintenance Signal classification System reliability Transportation systems Vibration analysis Vibration measurement Wavelet analysis Wavelet transforms |
title | Fault diagnosis of belt conveyor idlers based on gradient boosting decision tree |
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