Proceedings of the Annual Conference of the Prognostics and Health Management Society. Prognostics and Health Management Society. Conference最新文献

{"title":"Vibration Signal Decomposition using Dilated CNN","authors":"Eli Gildish, Michael Grebshtein, Yehudit Aperstein, Igor Makienko","doi":"10.36001/phmconf.2023.v15i1.3502","DOIUrl":"https://doi.org/10.36001/phmconf.2023.v15i1.3502","url":null,"abstract":"Vibration sensors have gained increasing popularity as valuable tools for Prognostics and Health Management (PHM) applications, enabling early detection of mechanical failures in industrial machines. Vibration signals comprise two main sources of information: periodic vibrations from components, phase-locked to the rotating speed (e.g., gears), and non-deterministic broadband vibrations associated with bearings, structure, and background noise. In PHM applications, it is important to decompose vibrations into these two sources to optimize the use of different diagnostic methods for each signal component. In practice, the decomposition should be cost-effective by working without supplementary information about system operating conditions and kinematics. Existing methods of vibration source separation commonly rely on an auto-regression (AR) model of vibrations and employ adaptive filtering techniques to estimate its parameters. However, these methods suffer from degraded accuracy in complex geared vibrations containing numerous periodic components and requiring large filter length to promise high frequency resolution in component separation. To address these challenges, we propose a new method that utilizes dilated Convolutional Neural Networks (CNNs) instead of adaptive filtering to improve the accuracy of decomposing complex vibration signals, all without the need for any supplementary information. To evaluate the performance of the new method, we conducted experiments using both simulated signals and real-world vibrations. The simulation results demonstrate improved accuracy in signal decomposition when our method is used instead of adaptive filtering. Additionally, the new method applied to real vibrations, showcases significant enhancement in bearing failure detection through accurate isolation of bearing-related vibrations. This study reveals the potential of our new method in various PHM applications requiring highly accurate diagnostics and prognostics in complex geared vibrations, particularly when supplementary information about operating conditions and system kinematics is unavailable.","PeriodicalId":91951,"journal":{"name":"Proceedings of the Annual Conference of the Prognostics and Health Management Society. Prognostics and Health Management Society. Conference","volume":"33 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136231882","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}

{"title":"An Introduction to 2023 PHM Data Challenge: The Elephant in the Room and an Analysis of Competition Results","authors":"Yongzhi Qu, Jesse William, Abhinav Saxena, Neil Eklund, Scott Clements","doi":"10.36001/phmconf.2023.v15i1.3814","DOIUrl":"https://doi.org/10.36001/phmconf.2023.v15i1.3814","url":null,"abstract":"The trend in diagnostics and prognostics for PHM is shifting toward explainable data-driven models. However, complex engineered systems are typically challenging to develop entirely explainable models for, whether they are grounded in physics or data-driven techniques. Consequently, the development of machine learning models, including hybrid variants capable of both interpolation and extrapolation, holds significant promise for enhancing the practicality of system simulation, analysis, modeling, and control in industry. The primary objective of this data challenge is to encourage contributions that expand the scope of model generalization beyond the training domain. The second aim of this data challenge is to quantify model uncertainty and methods to incorporate it into predictions. For most PHM tasks, clear guidance of the required action is ideal. To issue a definitive guidance to end users, it is useful to quantify uncertainty for the whole model. This data challenge addresses both estimation and uncertainty.","PeriodicalId":91951,"journal":{"name":"Proceedings of the Annual Conference of the Prognostics and Health Management Society. Prognostics and Health Management Society. Conference","volume":"92 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136317459","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}

{"title":"Sequential Hybrid Method for Full Lifetime Remaining Useful Life Prediction of Bearings in Rotating Machinery","authors":"None Koengeurts, Kerem Eryilmaz, Ted Oijevaar","doi":"10.36001/phmconf.2023.v15i1.3459","DOIUrl":"https://doi.org/10.36001/phmconf.2023.v15i1.3459","url":null,"abstract":"Optimal scheduling of the maintenance of bearings in rotating machinery requires accurate remaining useful life (RUL) prediction during the entire lifetime of the bearing. For that reason, this paper proposes a sequential hybrid method that combines the strengths of statistical and data-driven approaches. A statistical model-based approach is preferred before a bearing fault is detected, and a data-driven approach once a bearing fault is detected from the vibration measurements. The method is tested and evaluated on an extensive dataset of accelerated lifetime tests of deep groove ball bearings. It is shown that the method, with a limited amount of training data, delivers accurate RUL predictions during both the healthy stage of the bearing lifetime, as well as during the final stages of increasing degradation under both constant and varying speed conditions.","PeriodicalId":91951,"journal":{"name":"Proceedings of the Annual Conference of the Prognostics and Health Management Society. Prognostics and Health Management Society. Conference","volume":"61 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134972714","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}

{"title":"Comparison of Residual-based Methods on Fault Detection","authors":"Chi-Ching Hsu, Gaetan Frusque, Olga Fink","doi":"10.36001/phmconf.2023.v15i1.3444","DOIUrl":"https://doi.org/10.36001/phmconf.2023.v15i1.3444","url":null,"abstract":"An important initial step in fault detection for complex industrial systems is gaining an understanding of their health condition. Subsequently, continuous monitoring of this health condition becomes crucial to observe its evolution, track changes over time, and isolate faults. As faults are typically rare occurrences, it is essential to perform this monitoring in an unsupervised manner. Various approaches have been proposed not only to detect faults in an unsupervised manner but also to distinguish between different potential fault types. In this study, we perform a comprehensive comparison between two residual-based approaches: autoencoders, and theinput-output models that establish a mapping between operating conditions and sensor readings. We explore the sensorwise residuals and aggregated residuals for the entire system in both methods. The performance evaluation focuses on three tasks: health indicator construction, fault detection, and health indicator interpretation. To perform the comparison, we utilize the Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) dynamical model, specifically a subset of the turbofan engine dataset containing three different fault types. All models are trained exclusively on healthy data. Fault detection is achieved by applying a threshold that is determined based on the healthy condition. The detection results reveal that both models are capable of detecting faults with an average delay of around 20 cycles and maintain a low false positive rate. While the fault detection performance is similar for both models, the input-output model provides better interpretability regarding potential fault types and the possible faulty components.","PeriodicalId":91951,"journal":{"name":"Proceedings of the Annual Conference of the Prognostics and Health Management Society. Prognostics and Health Management Society. Conference","volume":"58 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134972717","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}

{"title":"A Closer Look at Bearing Fault Classification Approaches","authors":"Harika Abburi, Tanya Chaudhary, Sardar Haider Waseem Ilyas, Lakshmi Manne, Deepak Mittal, Edward Bowen, Don Williams, Derek Snaidauf, Balaji Veeramani","doi":"10.36001/phmconf.2023.v15i1.3473","DOIUrl":"https://doi.org/10.36001/phmconf.2023.v15i1.3473","url":null,"abstract":"Rolling bearing fault diagnosis has garnered increased attention in recent years owing to its presence in rotating machinery across various industries, and an ever increasing demand for efficient operations. Prompt detection and accurate prediction of bearing failures can help reduce the likelihood of unexpected machine downtime and enhance maintenance schedule, averting lost productivity. More recent technological advances have enabled monitoring the health of these assets at scale using a variety of sensors, and predicting the failures using modern machine learning (ML) approaches. Deep neural network approaches that has demonstrated superior performance across computer vision and natural language processing tasks are increasingly considered for failure prediction using vibration sensors. Vibration data has been collected using accelerated run-to-failure of overloaded bearings, or by introducing known failure in bearings, under a variety of operating conditions like rotating speed, load on the bearing, type of bearing fault, and data acquisition frequency. However, in using deep neural networks or other ML models for predicting failure using vibration data, there is a lack of consistency in the choice of how these approaches are evaluated, what portion of data from run-to-failure experiments are considered for training failure prediction models, or even how these problems are formulated as machine learning classification problems. An understanding of the impact of these choices is important to reliably develop models, and deploy them in practical settings. In this work, we demonstrate the significance of these choices on the performance of the models using publicly-available vibration datasets and discuss the implications of these choices for the models to be useful in real world scenarios.
","PeriodicalId":91951,"journal":{"name":"Proceedings of the Annual Conference of the Prognostics and Health Management Society. Prognostics and Health Management Society. Conference","volume":"16 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134972719","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}

{"title":"Accelerated Degradation Test on Electric Scroll Compressor Using Controlled Continuous Liquid Slugging","authors":"Hadyan Ramadhan, Hong Wong, Alaeddin Bani Milhim, Hossein Sadjadi","doi":"10.36001/phmconf.2023.v15i1.3525","DOIUrl":"https://doi.org/10.36001/phmconf.2023.v15i1.3525","url":null,"abstract":"Refrigerant-based electric scroll compressors are known for their reliability, efficiency, and quiet operation. They are often used in heat pump systems due to their ability to efficiently handle varying levels of load conditions, both for heating and cooling modes of operation. As electric compressors are considered the heart of the heat pump system, being able to determine degradation of compressors prior to failure is of paramount importance for the health of this system. Typical failures for electric scroll compressors range from electrical faults, refrigerant leaks, to mechanical failures and overheating. Specifically, one of the primary failure modes for an electric scroll compressor is mechanical damage due to the high stress effects of refrigerant liquid slugging. These stresses are due to excessively high internal pressures exhibited on the compressor scrolls, which are generated by compressing liquid refrigerant at the suction side of the compressor. This paper provides a new testing methodology that introduces liquid slugging at various degrees of refrigerant quality to degrade a compressor to near the end of useful life. Furthermore, this test aims to determine specific operating conditions and signals that can indicate early compressor degradation. This fault injection configuration consists of a modified heat pump system with the addition of two low pressure heat exchangers added in parallel (with respective electronically controlled expansion valve for each heat exchanger) used to control the refrigerant quality during compressor operations. For a given refrigerant quality, the heat pump system was operated at a fixed compressor performance conditions to sustain liquid slugging for a fixed duration. Afterwards, refrigerant was controlled to be pure vapor at the compressor suction side and the compressor was controlled at several different performance conditions (i.e., fixed compressor suction superheat temperature and compressor pressure ratios, at various compressor speeds), so as to duplicate conditions known to us from the compressor component data sheet for an ideal electric scroll compressor. Through these tests, the results show that the severity of scroll failures depend heavily on the refrigerant quality and the amount of liquid slugging exposure time. Furthermore, symptoms of compressor degradation are detected using the following signals: i) temperature and pressure at the compressor suction side, ii) temperature and pressure at the compressor discharge side, and iii) electric compressor speed and power consumption. To further aid in determining the compressor degradation ground truth, complete compressor teardown was performed to identify sections within the compressor that exhibited significant amounts of wear as compared to a stock compressor.","PeriodicalId":91951,"journal":{"name":"Proceedings of the Annual Conference of the Prognostics and Health Management Society. Prognostics and Health Management Society. Conference","volume":"65 12","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134907416","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}

{"title":"Automotive Electronic Control Unit Ground Line Health Monitoring Method","authors":"Alaeddin Milhim, Hadyan Ramadhan, Xinyu Du, Shengbing Jiang, Hossein Sadjadi","doi":"10.36001/phmconf.2023.v15i1.3520","DOIUrl":"https://doi.org/10.36001/phmconf.2023.v15i1.3520","url":null,"abstract":"Electronic Control Units (ECUs) have been used in the automotive industry for decades to control one or more of the vehicle subsystems. The ECUs communicate primarily using the in-vehicle Controller Area Network (CAN) communication protocol. The recent rapid development of connected, electric, and autonomous vehicles expands the number of ECUs and complexity of the CAN network required to integrate vehicle systems and deliver the desired functionalities. This demands increased reliability of the ECUs to ensure for robust vehicle performance. One of the most common ECU failure modes is the ECU ground fault. A ground fault occurs when the ground path in the ECU circuit is corroded, which is usually developed slowly over time. Such failure usually results in various symptoms including ECU incapable of functioning and further impacts the vehicle functionalities negatively. This type of fault can be difficult to detect prior to vehicle functionality loss. It usually involves routinely testing the resistance of the ground circuit, visually inspecting the connectors and wirings, and checking the voltage drop across the ground circuit. Therefore, it is highly desirable to continuously monitor the ECU ground line health status to predict any degradation and thus prevent vehicle functionality losses.
 This paper presents a novel method to monitor the health status of ECU ground line. The method leverages measured CAN voltage data to estimate the ECU ground state of health. The CAN voltage measurements are preprocessed and fed into a real-time data buffer of predefined size. Statistical moments are calculated from the buffered data to generate health indicators, which are then combined to form a fused health indicator. The fused health indicator is used to determine the health stage of ECU ground line. The health stage is classified based on the relationship between ground line degradation level and the ECU communication loss status. The method was developed and validated using actual vehicle data.","PeriodicalId":91951,"journal":{"name":"Proceedings of the Annual Conference of the Prognostics and Health Management Society. Prognostics and Health Management Society. Conference","volume":"24 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134907418","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}

{"title":"System-based Monitoring of Muscular Fatigue in Lower-Extremity Movement","authors":"Samuel Bertelson, Lindsey Molina, Richard Neptune, Dragan Djurdjanovic","doi":"10.36001/phmconf.2023.v15i1.3551","DOIUrl":"https://doi.org/10.36001/phmconf.2023.v15i1.3551","url":null,"abstract":"Physical fatigue accounts for many injuries in the workplace, sports arena, or battlefield. The traditional approaches to monitor fatigue rely on detecting and measuring shifts in the person’s muscular surface electromyography (sEMG) signals. However, assessing neuromuscular fatigue based purely on sEMG signals fails to account for the changing muscle dynamics during long dynamic physical tasks. To combat this dilemma, a system-based methodology has been recently developed and applied to several upper-extremity tasks. In this paper, we validate the efficacy of this novel methodology on the lower extremities during a dynamic activity. Specifically, the system-based monitoring methodology was applied to a cycling endurance task. It was statistically demonstrated that the system-based methodology resulted in a more-sensitive and less noisy metric, in comparison with an EMG-based methodology. The efficacy of the methodology was further illustrated by analyzing the inter-segmental recovering and fatiguing trends, which aligned with each muscle’s expected inter-muscle synergistic relationship.","PeriodicalId":91951,"journal":{"name":"Proceedings of the Annual Conference of the Prognostics and Health Management Society. Prognostics and Health Management Society. Conference","volume":"143 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134907421","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}

{"title":"Limitations and Opportunities in PHM for Offshore Wind Farms: A Socio-Technical-Ecological System Perspective","authors":"Arvind Keprate","doi":"10.36001/phmconf.2023.v15i1.3697","DOIUrl":"https://doi.org/10.36001/phmconf.2023.v15i1.3697","url":null,"abstract":"The burgeoning importance of offshore wind farms (OWFs) in the transition to sustainable energy systems underscores the need for effective Prognostics and Health Management (PHM) strategies. While the current PHM framework demonstrates its prowess in enhancing the reliability and operational efficiency of OWFs, this paper contends that its potential remains largely untapped due to certain inherent limitations. This study casts a comprehensive spotlight on the limitations and untapped opportunities within the PHM framework for OWFs from a Socio-Technical-Ecological Systems (SETS) perspective.
 The limitations, as identified, are threefold. First, the existing framework exhibits an over-reliance on technical factors, thus prioritizing maximization of Remaining Useful Life and cost minimization. This emphasis disregards crucial Non-Technological Factors (such as community impacts, stakeholder engagement, Human and Organization Factors (HOFs)) and uncertainty arising from them, which can exert significant influences on OWF’s health and performance. Second, the PHM approach often adopts a component-centric view, with focus on dominant degradation modes, thus undermining the intricate interdependencies among diverse components and failure modes. This lack of a System Level Perspective (SLP) and Multi-Modal Degradation (MMD) hampers a comprehensive understanding of how component degradation cascades through the entire system. Third, the current framework largely ignores the ecological considerations, despite compelling evidence that the current monitoring, assessment, and maintenance activities has significant ecological consequences.
 By addressing the identified limitations and leveraging the opportunities together with AI, the PHM framework for OWFs can evolve into a more comprehensive, inclusive, and resilient approach. The proposed paradigm shift resonates deeply with the contemporary drive towards sustainability, not only in terms of technical efficacy but also in terms of social acceptance and ecological compatibility.","PeriodicalId":91951,"journal":{"name":"Proceedings of the Annual Conference of the Prognostics and Health Management Society. Prognostics and Health Management Society. Conference","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134907239","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}

{"title":"Interpolate and Extrapolate Machine Learning Models using An Unsupervised Method","authors":"Peng Liu","doi":"10.36001/phmconf.2023.v15i1.3794","DOIUrl":"https://doi.org/10.36001/phmconf.2023.v15i1.3794","url":null,"abstract":"The 2023 PHM North America Data Challenge is intriguing because it requires one to predict outcomes and use data patterns that training models do not see. Modern machine learning models based on gradient boosting and neural networks are not designed to address such issues in usually circumstances. Our final approach to address the challenge consists of five steps. In our approach, we use an unsupervised method besides machine learning models to address the challenge.","PeriodicalId":91951,"journal":{"name":"Proceedings of the Annual Conference of the Prognostics and Health Management Society. Prognostics and Health Management Society. Conference","volume":"69 7","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134907413","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}