Pub Date : 2024-07-21DOI: 10.36001/ijphm.2024.v15i2.3851
Abbas Al-Refaie, Hedayeh Aljundi
The productivity and efficiency of industrial systems are highly affected by failures and machine breakdowns. Further, in asset-intensive industries, unexpected failures are considered the primary source of operational risk. In response, the maintenance department strives to calculate reliable estimates of the risk levels associated with such failures and develop resilient maintenance strategies that enable it to respond effectively to equipment failures. The research developed a framework for integrating fuzzy failure mode and effects analysis (FFMEA) with resilience engineering (RE) concepts for maintenance planning. The framework consists of four main stages: FFMEA, Risk iso-surface (RI), resilience assessment, and maintenance planning. In FFMEA, multiple sub-factors were considered for each main risk factor and evaluated using fuzzy logic. Then, in the RI stage, the risk priority number (RPN) was calculated through a fuzzy approach that considered the order of the importance of the main three risk factors. The fuzzy resilience assessment was applied through a survey of fifty-one questions related to the main four RE potentials to determine the need for resilient maintenance strategies. Finally, the RPN-Resilience diagram was employed to classify maintenance activities into six main maintenance strategies. A case study from a production line of plastic bags was used for illustration. The main advantage of the proposed FFMEA is that it divides the main risk criteria into sub-criteria to increase the accuracy of risk assessment and evaluate resilience potentials under fuzziness. In conclusion, the integration of the risk-resilience evaluation is a valuable tool for effectively planning maintenance activities.
{"title":"Fuzzy FMEA-Resilience Approach for Maintenance Planning in a Plastics Industry ","authors":"Abbas Al-Refaie, Hedayeh Aljundi","doi":"10.36001/ijphm.2024.v15i2.3851","DOIUrl":"https://doi.org/10.36001/ijphm.2024.v15i2.3851","url":null,"abstract":"The productivity and efficiency of industrial systems are highly affected by failures and machine breakdowns. Further, in asset-intensive industries, unexpected failures are considered the primary source of operational risk. In response, the maintenance department strives to calculate reliable estimates of the risk levels associated with such failures and develop resilient maintenance strategies that enable it to respond effectively to equipment failures. The research developed a framework for integrating fuzzy failure mode and effects analysis (FFMEA) with resilience engineering (RE) concepts for maintenance planning. The framework consists of four main stages: FFMEA, Risk iso-surface (RI), resilience assessment, and maintenance planning. In FFMEA, multiple sub-factors were considered for each main risk factor and evaluated using fuzzy logic. Then, in the RI stage, the risk priority number (RPN) was calculated through a fuzzy approach that considered the order of the importance of the main three risk factors. The fuzzy resilience assessment was applied through a survey of fifty-one questions related to the main four RE potentials to determine the need for resilient maintenance strategies. Finally, the RPN-Resilience diagram was employed to classify maintenance activities into six main maintenance strategies. A case study from a production line of plastic bags was used for illustration. The main advantage of the proposed FFMEA is that it divides the main risk criteria into sub-criteria to increase the accuracy of risk assessment and evaluate resilience potentials under fuzziness. In conclusion, the integration of the risk-resilience evaluation is a valuable tool for effectively planning maintenance activities.","PeriodicalId":42100,"journal":{"name":"International Journal of Prognostics and Health Management","volume":null,"pages":null},"PeriodicalIF":1.4,"publicationDate":"2024-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141818541","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-05-06DOI: 10.36001/ijphm.2024.v15i1.3808
Rik Vaerenberg, Douw Marx, Seyed Ali Hosseinli, Fabrizio De Fabritiis, Hao Wen, Rui Zhu, Konstantinos C. Gryllias
Gear pitting is a common gearbox failure mode that can lead to unplanned machine downtime, inefficient power transmission and a higher risk of sudden catastrophic failure. Consequently, there is strong incentive to create machine learning models that are capable of detecting and quantifying the severity of gearbox pitting faults. The performance of machine learning models is however highly dependent on the availability of training data and since training data for a wide variety of different operating conditions and fault severities is rarely available in practice, machine learning models must be designed to be robust to unseen operating conditions and fault severities. Furthermore, models should be capable of identifying data outside of the training data distribution and adjusting the confidence in a prediction accordingly. This work presents a strategy for pitting severity estimation in gearboxes under unseen operating conditions and fault severities in response to the PHM North America 2023 Conference Data Challenge. The strategy includes the design of dedicated validation sets for quantifying model performance on unseen data, an investigation into the most appropriate preprocessing methods, and a specialized convolutional neural network with an integrated out of distribution detection model for identifying samples from foreign operating conditions and fault severities. The results show that the best models are capable of some generalization to unseen operating conditions, but the generalization to unseen pitting severities is more challenging.
{"title":"Preprocessing and Modeling Approach for Gearbox Pitting Severity Prediction under Unseen Operating Conditions and Fault Severities","authors":"Rik Vaerenberg, Douw Marx, Seyed Ali Hosseinli, Fabrizio De Fabritiis, Hao Wen, Rui Zhu, Konstantinos C. Gryllias","doi":"10.36001/ijphm.2024.v15i1.3808","DOIUrl":"https://doi.org/10.36001/ijphm.2024.v15i1.3808","url":null,"abstract":"Gear pitting is a common gearbox failure mode that can lead to unplanned machine downtime, inefficient power transmission and a higher risk of sudden catastrophic failure. Consequently, there is strong incentive to create machine learning models that are capable of detecting and quantifying the severity of gearbox pitting faults. The performance of machine learning models is however highly dependent on the availability of training data and since training data for a wide variety of different operating conditions and fault severities is rarely available in practice, machine learning models must be designed to be robust to unseen operating conditions and fault severities. Furthermore, models should be capable of identifying data outside of the training data distribution and adjusting the confidence in a prediction accordingly. This work presents a strategy for pitting severity estimation in gearboxes under unseen operating conditions and fault severities in response to the PHM North America 2023 Conference Data Challenge. The strategy includes the design of dedicated validation sets for quantifying model performance on unseen data, an investigation into the most appropriate preprocessing methods, and a specialized convolutional neural network with an integrated out of distribution detection model for identifying samples from foreign operating conditions and fault severities. The results show that the best models are capable of some generalization to unseen operating conditions, but the generalization to unseen pitting severities is more challenging.","PeriodicalId":42100,"journal":{"name":"International Journal of Prognostics and Health Management","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2024-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141010764","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-18DOI: 10.36001/ijphm.2024.v15i1.3829
Ahmed Al-Ajeli, Eman S. Alshamery
In this work, a method for fault diagnosis and localization is proposed. This method adopts the long short-term memory (LSTM) neural network to detect, isolate and determine the component of the system in which a fault has occurred. Unlike the traditional methods used for fault diagnosis, which first extract features from the raw data and then use a classifier in order to diagnose the fault; the LSTM-based method works directly on raw data and builds the classifier. This can be accomplished by training the neural network using the raw data resulting in a trained model (classifier) capturing generalized patterns from this data. This model is used online to diagnose faults and determine the faulty component. The performance of the resulting model is evaluated on testing data. The proposed method has been applied to real time-series data representing sensor readings in spacecraft electrical power distribution systems. The experimental results show promising performance in separating fault modes and identifying the faulty components.
{"title":"Multi-Output Deep Learning Model for Fault Diagnosis Based on Time-Series Data","authors":"Ahmed Al-Ajeli, Eman S. Alshamery","doi":"10.36001/ijphm.2024.v15i1.3829","DOIUrl":"https://doi.org/10.36001/ijphm.2024.v15i1.3829","url":null,"abstract":"In this work, a method for fault diagnosis and localization is proposed. This method adopts the long short-term memory (LSTM) neural network to detect, isolate and determine the component of the system in which a fault has occurred. Unlike the traditional methods used for fault diagnosis, which first extract features from the raw data and then use a classifier in order to diagnose the fault; the LSTM-based method works directly on raw data and builds the classifier. This can be accomplished by training the neural network using the raw data resulting in a trained model (classifier) capturing generalized patterns from this data. This model is used online to diagnose faults and determine the faulty component. The performance of the resulting model is evaluated on testing data. The proposed method has been applied to real time-series data representing sensor readings in spacecraft electrical power distribution systems. The experimental results show promising performance in separating fault modes and identifying the faulty components.","PeriodicalId":42100,"journal":{"name":"International Journal of Prognostics and Health Management","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2024-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140688815","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-17DOI: 10.36001/ijphm.2024.v15i1.3818
Maha Ben Ayed, M. Soualhi, Raouf Ketata, N. Mairot, Sylvian Giampiccolo, Noureddine Zerhouni
Data-driven Prognostics and Health Management (PHM) become a crucial layer in the realm of predictive maintenance (PM). However, many industries develop PM technologies based on the monitoring of machine data to anticipate failures without considering the injected raw material. In reality, non-compliant material characteristics can affect the manufacturing tools leading to machine breakdowns and poor quality product. To cope with this situation, this paper proposes a new methodology that helps operators predicting machine breakdowns. In detail, the methodology starts by implementing an Extract, Transform, Load (ETL) process which aims to create a new and reliable dataset from heterogeneous sources. Then, a feature selection method is used for dimensionality reduction and keep only useful information. After that, the selected features are injected to Machine Learning (ML) algorithms to predict system breakdown occurrences. Finally, the novelty in this study, an auto-labeling algorithm based on material data and machine breakdown predictions is proposed. This algorithm aims to enhance raw material stock management, scheduling their consumption accordingly and thus reducing machine breakdowns. The developed methodology is applied to a real dataset of a French company, SCODER, that shows and pointed out promising perspectives in PM.
{"title":"Data-Driven Methodology to Assess Raw Materials Impact on Manufacturing Systems Breakdowns","authors":"Maha Ben Ayed, M. Soualhi, Raouf Ketata, N. Mairot, Sylvian Giampiccolo, Noureddine Zerhouni","doi":"10.36001/ijphm.2024.v15i1.3818","DOIUrl":"https://doi.org/10.36001/ijphm.2024.v15i1.3818","url":null,"abstract":"Data-driven Prognostics and Health Management (PHM) become a crucial layer in the realm of predictive maintenance (PM). However, many industries develop PM technologies based on the monitoring of machine data to anticipate failures without considering the injected raw material. In reality, non-compliant material characteristics can affect the manufacturing tools leading to machine breakdowns and poor quality product. To cope with this situation, this paper proposes a new methodology that helps operators predicting machine breakdowns. In detail, the methodology starts by implementing an Extract, Transform, Load (ETL) process which aims to create a new and reliable dataset from heterogeneous sources. Then, a feature selection method is used for dimensionality reduction and keep only useful information. After that, the selected features are injected to Machine Learning (ML) algorithms to predict system breakdown occurrences. Finally, the novelty in this study, an auto-labeling algorithm based on material data and machine breakdown predictions is proposed. This algorithm aims to enhance raw material stock management, scheduling their consumption accordingly and thus reducing machine breakdowns. The developed methodology is applied to a real dataset of a French company, SCODER, that shows and pointed out promising perspectives in PM.","PeriodicalId":42100,"journal":{"name":"International Journal of Prognostics and Health Management","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2024-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140694650","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-03-12DOI: 10.36001/ijphm.2024.v15i1.3590
Viswajith S Nair, R. K, Saravanamurugan S
The generation of chatter during machining operations is extremely detrimental to the cutting tool life and the surface quality of the workpiece. The present study aims to identify chatter conditions during the end milling of Ti6Al4V alloy. Experimental modal analysis is carried out, and stability lobe diagrams (SLDs) are developed to identify machining parameters under stable and chatter conditions. Experiments are conducted to acquire cutting force and vibration signatures corresponding to machining conditions selected from the SLD. Non-linear chatter features, such as Approximate Entropy, Holder Exponent, and Lyapunov Exponent extracted from the sensor signatures, are used to build Machine Learning (ML) models to identify chatter using Decision Trees (DTs), Support Vector Machines (SVMs) and DT-based Ensembles. A feature-level fusion approach is adopted to improve the classification performance of the ML models. The DT-based Adaboost model trained using dominant non-linear features classifies chatter with an accuracy of 96.8%. The non-linear features extracted from the sensor signatures offer a direct indication of the chatter and are found to be effective in identifying the machining chatter with good accuracy.
{"title":"Chatter Identification in Milling of Titanium Alloy Using Machine Learning Approaches with Non-Linear Features of Cutting Force and Vibration Signatures","authors":"Viswajith S Nair, R. K, Saravanamurugan S","doi":"10.36001/ijphm.2024.v15i1.3590","DOIUrl":"https://doi.org/10.36001/ijphm.2024.v15i1.3590","url":null,"abstract":"The generation of chatter during machining operations is extremely detrimental to the cutting tool life and the surface quality of the workpiece. The present study aims to identify chatter conditions during the end milling of Ti6Al4V alloy. Experimental modal analysis is carried out, and stability lobe diagrams (SLDs) are developed to identify machining parameters under stable and chatter conditions. Experiments are conducted to acquire cutting force and vibration signatures corresponding to machining conditions selected from the SLD. Non-linear chatter features, such as Approximate Entropy, Holder Exponent, and Lyapunov Exponent extracted from the sensor signatures, are used to build Machine Learning (ML) models to identify chatter using Decision Trees (DTs), Support Vector Machines (SVMs) and DT-based Ensembles. A feature-level fusion approach is adopted to improve the classification performance of the ML models. The DT-based Adaboost model trained using dominant non-linear features classifies chatter with an accuracy of 96.8%. The non-linear features extracted from the sensor signatures offer a direct indication of the chatter and are found to be effective in identifying the machining chatter with good accuracy.","PeriodicalId":42100,"journal":{"name":"International Journal of Prognostics and Health Management","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2024-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140250312","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-03-05DOI: 10.36001/ijphm.2024.v15i1.3826
Roberto Diversi, Nicolò Speciale, Matteo Barbieri
This paper addresses the problem of monitoring the state of health of electric motor driven mechanisms. The proposed condition monitoring procedure belongs to the data-driven methods and employs a combination of wavelet analysis and autoregressive model identification. It exploits the fact that the torque motor signal is a readily available measurement in industrial computers complying with the PLCOpen standard and how motion controllers execute electric cams. In particular, the torque provided by the PLC is represented as the sum between the ideal torque and an additional contribution that contains information about mechanism health condition. The procedure completely removes the ideal torque and analyzes the residual component to highlight and classify possible fault conditions. The described condition monitoring procedure is tested on real data in a laboratory setup.
{"title":"Combining Wavelets and AR Identification for Condition Monitoring of Electric-cam Mechanisms Using PLCopen Readings of Motor Torque","authors":"Roberto Diversi, Nicolò Speciale, Matteo Barbieri","doi":"10.36001/ijphm.2024.v15i1.3826","DOIUrl":"https://doi.org/10.36001/ijphm.2024.v15i1.3826","url":null,"abstract":"This paper addresses the problem of monitoring the state of health of electric motor driven mechanisms. The proposed condition monitoring procedure belongs to the data-driven methods and employs a combination of wavelet analysis and autoregressive model identification. It exploits the fact that the torque motor signal is a readily available measurement in industrial computers complying with the PLCOpen standard and how motion controllers execute electric cams. In particular, the torque provided by the PLC is represented as the sum between the ideal torque and an additional contribution that contains information about mechanism health condition. The procedure completely removes the ideal torque and analyzes the residual component to highlight and classify possible fault conditions. The described condition monitoring procedure is tested on real data in a laboratory setup.","PeriodicalId":42100,"journal":{"name":"International Journal of Prognostics and Health Management","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2024-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140079492","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-03-03DOI: 10.36001/ijphm.2024.v15i1.3799
Boubker Najdi, M. Benbrahim, M. Kabbaj
Bearings are critical components in rotating machinery, and their failure can lead to costly repairs and downtime. To prevent such failures, it is important to detect and diagnose bearing faults early. In recent years, deep-learning techniques have shown promise for detecting and diagnosing bearing faults automatically. While these algorithms can all achieve diagnostic accuracy of over 90%, their generalizability and robustness in complex, extreme variable loading conditions have not been thoroughly validated. In this paper, a feature extraction method based on Synchro-squeezing Wavelet Transform (SSWT), Random projection (RP), and deep learning (DL) is presented. To fulfill the data requirements of neural networks, data augmentation is initially utilized to augment the size of the original data. Subsequently, the SSWT technique is employed to convert the signals from the Time domain to the Time-Frequency domain, resulting in the conversion of the 1-D signal to a 2-D feature image. To decrease the complexity of deep learning computation, data preprocessing involves utilizing Random projection to reduce feature dimensionality. The final step involves constructing a Convolutional Neural Network (CNN) model that can identify fault features from the obtained Time-Frequency images and perform accurate fault classification. By utilizing the CWRU and IMS datasets to evaluate the method, the study demonstrates that the suggested approach outperforms advanced techniques in terms of both diagnostic accuracy and robustness.
{"title":"Bearing Fault Diagnosis under Varying Work Conditions Based on Synchrosqueezing Transform, Random Projection, and Convolutional Neural Networks","authors":"Boubker Najdi, M. Benbrahim, M. Kabbaj","doi":"10.36001/ijphm.2024.v15i1.3799","DOIUrl":"https://doi.org/10.36001/ijphm.2024.v15i1.3799","url":null,"abstract":"Bearings are critical components in rotating machinery, and their failure can lead to costly repairs and downtime. To prevent such failures, it is important to detect and diagnose bearing faults early. In recent years, deep-learning techniques have shown promise for detecting and diagnosing bearing faults automatically. While these algorithms can all achieve diagnostic accuracy of over 90%, their generalizability and robustness in complex, extreme variable loading conditions have not been thoroughly validated. In this paper, a feature extraction method based on Synchro-squeezing Wavelet Transform (SSWT), Random projection (RP), and deep learning (DL) is presented. To fulfill the data requirements of neural networks, data augmentation is initially utilized to augment the size of the original data. Subsequently, the SSWT technique is employed to convert the signals from the Time domain to the Time-Frequency domain, resulting in the conversion of the 1-D signal to a 2-D feature image. To decrease the complexity of deep learning computation, data preprocessing involves utilizing Random projection to reduce feature dimensionality. The final step involves constructing a Convolutional Neural Network (CNN) model that can identify fault features from the obtained Time-Frequency images and perform accurate fault classification. By utilizing the CWRU and IMS datasets to evaluate the method, the study demonstrates that the suggested approach outperforms advanced techniques in terms of both diagnostic accuracy and robustness.","PeriodicalId":42100,"journal":{"name":"International Journal of Prognostics and Health Management","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2024-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140081057","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-26DOI: 10.36001/ijphm.2024.v15i1.3589
Markus Ulmer, Jannik Zgraggen, L. G. Huber
Anomaly detection (AD) tasks have been solved using machine learning algorithms in various domains and applications. The great majority of these algorithms use normal data to train a residual-based model, and assign anomaly scores to unseen samples based on their dissimilarity with the learned normal regime. The underlying assumption of these approaches is that anomaly-free data is available for training. This is, however, often not the case in real-world operational settings, where the training data may be contaminated with a certain fraction of abnormal samples. Training with contaminated data, in turn, inevitably leads to a deteriorated AD performance of the residual-based algorithms. In this paper we introduce a framework for a fully unsupervised refinement of contaminated training data for AD tasks. The framework is generic and can be applied to any residual-based machine learning model. We demonstrate the application of the framework to two public datasets of multivariate time series machine data from different application fields. We show its clear superiority over the naive approach of training with contaminated data without refinement. Moreover, we compare it to the ideal, unrealistic reference in which anomaly-free data would be available for training. Since the approach exploits information from the anomalies, and not only from the normal regime, it is comparable and often outperforms the ideal baseline as well.
在各种领域和应用中，异常检测（AD）任务都是通过机器学习算法来解决的。这些算法中的绝大多数都使用正常数据来训练基于残差的模型，并根据未见样本与所学正常机制的不相似性为其分配异常分数。这些方法的基本假设是，无异常数据可用于训练。然而，在实际操作环境中，情况往往并非如此，训练数据可能会受到一部分异常样本的污染。反过来，使用受污染的数据进行训练必然会导致基于残差的算法的 AD 性能下降。在本文中，我们介绍了一个框架，用于在完全无监督的情况下完善 AD 任务中受污染的训练数据。该框架具有通用性，可应用于任何基于残差的机器学习模型。我们在两个来自不同应用领域的多变量时间序列机器数据的公共数据集上演示了该框架的应用。我们展示了其明显优于使用污染数据进行训练而不进行细化的天真方法。此外，我们还将其与理想的、不现实的参考方法进行了比较，在后者中，无异常数据可用于训练。由于该方法利用的是异常信息，而不仅仅是正常状态下的信息，因此与理想的基准线不相上下，而且往往更胜一筹。
{"title":"Generic Machine Learning Framework for Fully-Unsupervised Anomaly Detection with Contaminated Data","authors":"Markus Ulmer, Jannik Zgraggen, L. G. Huber","doi":"10.36001/ijphm.2024.v15i1.3589","DOIUrl":"https://doi.org/10.36001/ijphm.2024.v15i1.3589","url":null,"abstract":"Anomaly detection (AD) tasks have been solved using machine learning algorithms in various domains and applications. The great majority of these algorithms use normal data to train a residual-based model, and assign anomaly scores to unseen samples based on their dissimilarity with the learned normal regime. The underlying assumption of these approaches is that anomaly-free data is available for training. This is, however, often not the case in real-world operational settings, where the training data may be contaminated with a certain fraction of abnormal samples. Training with contaminated data, in turn, inevitably leads to a deteriorated AD performance of the residual-based algorithms. \u0000In this paper we introduce a framework for a fully unsupervised refinement of contaminated training data for AD tasks. The framework is generic and can be applied to any residual-based machine learning model. We demonstrate the application of the framework to two public datasets of multivariate time series machine data from different application fields. We show its clear superiority over the naive approach of training with contaminated data without refinement. Moreover, we compare it to the ideal, unrealistic reference in which anomaly-free data would be available for training. Since the approach exploits information from the anomalies, and not only from the normal regime, it is comparable and often outperforms the ideal baseline as well.","PeriodicalId":42100,"journal":{"name":"International Journal of Prognostics and Health Management","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2024-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139594481","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-17DOI: 10.36001/ijphm.2024.v15i1.3791
Abdel wahhab Lourari, T. Benkedjouh, Bilal El Yousfi, A. Soualhi
Bearings are critical components extensively used in rotary machines, often being the leading cause of unexpected machine shutdowns. To mitigate system failures, it is crucial to implement effective maintenance strategies. This paper introduces a novel methodology for bearing prognostics, employing Wavelet Packet Decomposition (WPD) for data preprocessing, Sequential Backward Selection (SBS) for feature selection, and Adaptive Neuro-Fuzzy Inference System (ANFIS) networks for prognostic modeling. The proposed approach consists of two key steps. Firstly, the data undergoes preprocessing through Wavelet Packet Decomposition, enhancing the quality and extracting relevant features. Subsequently, the Remaining Useful Life (RUL) of the bearing is predicted using a degradation model. The accuracy of the proposed method is evaluated using a bearing life dataset obtained from a run-to-failure test (IMS dataset). The results demonstrate the remarkable capability of the ANFIS model to learn and accurately estimate the system’s RUL. By leveraging the combined power of WPD, SBS, and ANFIS, this methodology showcases its potential as an effective prognostic tool for bearing health assessment and proactive maintenance planning.
{"title":"ANFIS-based Framework for the Prediction of Bearing’s Remaining Useful Life","authors":"Abdel wahhab Lourari, T. Benkedjouh, Bilal El Yousfi, A. Soualhi","doi":"10.36001/ijphm.2024.v15i1.3791","DOIUrl":"https://doi.org/10.36001/ijphm.2024.v15i1.3791","url":null,"abstract":"Bearings are critical components extensively used in rotary machines, often being the leading cause of unexpected machine shutdowns. To mitigate system failures, it is crucial to implement effective maintenance strategies. This paper introduces a novel methodology for bearing prognostics, employing Wavelet Packet Decomposition (WPD) for data preprocessing, Sequential Backward Selection (SBS) for feature selection, and Adaptive Neuro-Fuzzy Inference System (ANFIS) networks for prognostic modeling. The proposed approach consists of two key steps. Firstly, the data undergoes preprocessing through Wavelet Packet Decomposition, enhancing the quality and extracting relevant features. Subsequently, the Remaining Useful Life (RUL) of the bearing is predicted using a degradation model. The accuracy of the proposed method is evaluated using a bearing life dataset obtained from a run-to-failure test (IMS dataset). The results demonstrate the remarkable capability of the ANFIS model to learn and accurately estimate the system’s RUL. By leveraging the combined power of WPD, SBS, and ANFIS, this methodology showcases its potential as an effective prognostic tool for bearing health assessment and proactive maintenance planning.","PeriodicalId":42100,"journal":{"name":"International Journal of Prognostics and Health Management","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2024-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139527311","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-21DOI: 10.36001/ijphm.2023.v14i2.3587
Kajetan Fricke, R. Nascimento, Matteo Corbetta, Chetan S. Kulkarni, Felipe A. C. Viana
The development of new modes of transportation, such as electric vertical takeoff and landing (eVTOL) aircraft and the use of drones for package and medical delivery, has increased the demand for reliable and powerful electric batteries. The most common batteries in electric-powered vehicles use Lithium-ion (Li-ion). Because of their long cycle life, they are the preferred choice for battery packs deployed over a lifespan of many years. Thus, battery aging needs to be well understood to achieve safe and reliable operation, and life cycle experiments are a crucial tool to characterize the effect of degradation and failure. With the importance of battery durability in mind, we present an accelerated Li-ion battery life cycle data set, focused on a large range of load levels, for batteries composed of two 18650 cells. We tested 26 battery packs grouped by: (i) constant or random loading conditions, (ii) loading levels, and (iii) number of load level changes. Furthermore, we conducted load cycling on second-life batteries, where surviving cells from previously-aged packs were assembled to second-life packs. The goal is to provide the PHM community with an additional data set characterized by unique features. The aggressive load profiles create large temperature increases within the cells. Temperature effects becomes therefore important for prognosis. Some samples are subject to changes in amplitude and number of load levels, thus approaching the level of variability encountered in real operations. Reassembling of survival cells into new packs created additional data that can be used to evaluate the performance of recommissioned batteries. The data set can be leveraged to develop and test models for state-of-charge and state-of-health prognosis. This paper serves as a companion to the data set. It outlines the design of experiment, shows some exemplifying time-series voltage curves and aging data, describes the testbed design and capabilities, and also provides information about the outliers detected thus far. Upon acceptance, the data set will be made available on the NASA Ames Prognostics Center of Excellence Data Repository.
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