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

{"title":"Grey-box Approach for the Prognostic and Health Management of Lithium-Ion Batteries","authors":"Francesco Cancelliere, Sylvain Girard, Jean-Marc Bourinet, Matteo Broggi","doi":"10.36001/phmconf.2023.v15i1.3506","DOIUrl":"https://doi.org/10.36001/phmconf.2023.v15i1.3506","url":null,"abstract":"The Lithium-Ion Batteries (LIB) industry is rapidly growing and is expected to continue expanding exponentially in the next decade. LIBs are already widely used in everyday life, and their demand is expected to increase further, particularly in the automotive sector. The European Union has introduced a new law to ban Internal Combustion Engines from 2035, pushing for the adoption of electric vehicles and increasing the need for more efficient and reliable energy storage solutions such as LIBs. As a result, the establishment of Gigafactories in Europe and the United States is accelerating to meet the growing demand and partially reduce dependencies on China, which is currently the main producer of LIBs.
 To fully realize the potential of LIBs and ensure their safe and sustainable use, it is crucial to optimize their useful life and develop reliable and robust methodologies for estimating their state of health and predicting their remaining useful life. This requires a comprehensive understanding of LIB behavior and the development of effective prognostic and health management approaches that can accurately predict battery degradation, plan for maintenance and replacements, and improve battery performance and lifespan.
 This work, funded by the GREYDIENT project, a European consortium aiming to advance the state of the art in the grey-box approach, combines physical modeling (white box) and machine learning (black box) techniques to demonstrate the grey-box effectiveness in the Prognostic and Health Management. The grey-box approach here proposed consist in a combination of a physical battery model whose degradation parameters are estimated online at every cycle by a Multi-Layer Perceptron Particle Filter (MLP-PF).
 An electrochemical degradation model of a Lithium-Ion battery cell has been derived by use of Modelica. The model simulates the output voltage of the cell, while the degradation over time is simulate through the variation of 3 parameters: qMax (maximum number of Lithium-Ions available), R0 (Internal Resistance) and D (Diffusion Coefficient). To validate the model we resorted to the well-known NASA Battery Dataset, which has also been used to infer the optimal values of the three hidden degradation parameters at every cycle, to obtain their Run-to-Failure history. Then, the physical model is combined the MLP-PF: a MLPArtificial Neural Network is firstly trained on the Run-to-Failure degradation processes of the model parameters, allowing the propagation of the parameters in the future and the corresponding estimation of the battery Remaining Use ful Life (RUL). The MLP is then updated online by a Particle Filter every time a new measurement is available from the Battery Management System (BMS), providing flexibility to this method, needed for the electrochemical nature of the batteries, and allowing the propagation of uncertainties.","PeriodicalId":91951,"journal":{"name":"Proceedings of the Annual Conference of the Prognostics and Health Management Society. Prognostics and Health Management Society. Conference","volume":"39 5","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134907585","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":"Underlying Probability Measure Approximated by Monte Carlo Simulations in Event Prognostics","authors":"David Acuña-Ureta, Marcos Orchard","doi":"10.36001/phmconf.2023.v15i1.3536","DOIUrl":"https://doi.org/10.36001/phmconf.2023.v15i1.3536","url":null,"abstract":"The prognostic of events, and particularly of failures, is a key step towards allowing preventive decision-making, as in the case of predictive maintenance in Industry 4.0, for example. However, the occurrence time of a future event is subject to uncertainty, so it is natural to think of it as a random variable. In this regard, the default procedure (benchmark) to compute its probability distribution is empirical, through Monte Carlo simulations. Nonetheless, the analytic expression for the probability distribution of the occurrence time of any future event was presented and demonstrated in a recent publication. In this article it is established a direct relationship between these empirical and analytical procedures. It is shown that Monte Carlo simulations numerically approximate the analytically known probability measure when the future event is triggered by the crossing of a threshold.","PeriodicalId":91951,"journal":{"name":"Proceedings of the Annual Conference of the Prognostics and Health Management Society. Prognostics and Health Management Society. Conference","volume":"4 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":"134907588","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":"Battery State-of-Health Aware Path Planning for a Mars Rover","authors":"Mariana Salinas-Camus, Chetan Kulkarni, Marcos Orchard","doi":"10.36001/phmconf.2023.v15i1.3511","DOIUrl":"https://doi.org/10.36001/phmconf.2023.v15i1.3511","url":null,"abstract":"A rover mission consists of visiting waypoints to gather scientific samples based on set requirements. However, rovers face operational uncertainties during the mission, affecting the performance of its electrical and mechanical components and overall mission success. Hence, it is critical to have a decision-making framework that is aware of the health state of the components when planning the path of the vehicle. In particular, battery degradation, and consequently the battery State of Health (SOH), can affect the optimality of decisions made by the autonomous system in the long term. This paper presents a decision-making system that incorporates information on the energy drawn from the battery (based on the velocity of the vehicle), terrain conditions, and model-based prognostic modules to assess impact on the battery state of charge (SoC). The decision-making system was formulated as a Markov Decision Process (MDP) to reach the goal destination by sending commands in a determined amount of time, while maintaining the battery SoC within the policy stated. The MDP problem was programmed using the open-source framework POMDPs.jl, which has a variety of online and offline solvers. To solve the MDP problem online, we used Monte Carlo Tree Search (MCTS). Results from simulations demonstrate the effect that battery degradation and charging plans have on decision-making.","PeriodicalId":91951,"journal":{"name":"Proceedings of the Annual Conference of the Prognostics and Health Management Society. Prognostics and Health Management Society. Conference","volume":"39 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":"134907591","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":"Adaptive Prognostics: A reliable RUL approach","authors":"Nick Eleftheroglou","doi":"10.36001/phmconf.2023.v15i1.3495","DOIUrl":"https://doi.org/10.36001/phmconf.2023.v15i1.3495","url":null,"abstract":"Prognostic methodologies have found increasing use the last decade and provide a platform for remaining useful life (RUL) predictions of engineering systems utilizing condition monitoring data. Of particular interest is the reliable RUL prediction of engineering assets that either underperform or outperform due to unexpected phenomena that might occur during the operational life. These assets are often referred as outliers and the prediction of their RUL is a challenging task. The challenge is to accurately predict the RUL of an outlier without taking into account outlier’s condition monitoring data in the training process but just in the testing process. As a result, the lifetime of the testing asset is shorter (left outlier) or longer (right outlier) than the training process’ lifetimes.
 This study addresses this challenge by proposing a new adaptive model; the Similarity Learning Hidden Semi Markov Model (SLHSMM), which is an extension of the Non-Homogenous Hidden Semi Markov Model (NHHSMM). The SLHSMM uses a similarity function, such as Minkowski distances, in order firstly to quantify the similarity between the testing asset and each training asset and secondly to adapt the trained parameters of the NHHSMM. To demonstrate the effectiveness of the proposed adaptive methodology, composite structures have been used as a validation engineering asset. In particular, the training data set consists of strain data collected from open-hole carbon–epoxy specimens, which were subjected to fatigue loading only, while the testing data set consists of strain data collected from specimens that were subjected to fatigue and in-situ impact loading, which can be considered as an unexpected phenomenon and unseen event regarding the training process. 
 Utilizing the aforementioned strain data the SLHSMM RUL predictions and the NHHSMM RUL predictions were compared, so as to verify that the SLHSMM provides better prognostics than the NHHSMM. SLHSMM provides better predictions in comparison to the NHHSMM for all the test cases, demonstrating its capability to adapt to unexpected phenomena and integrate unforeseen data to the prognostics course.","PeriodicalId":91951,"journal":{"name":"Proceedings of the Annual Conference of the Prognostics and Health Management Society. Prognostics and Health Management Society. Conference","volume":"144 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":"134907756","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":"Gear Pitting Fault Diagnosis Using Domain Generalizations and Specialization Techniques","authors":"Fan Chu","doi":"10.36001/phmconf.2023.v15i1.3812","DOIUrl":"https://doi.org/10.36001/phmconf.2023.v15i1.3812","url":null,"abstract":"Gear pitting is a common gear fault, which has been an important subject to industry and research community, In the past, the diagnosis of gear pitting faults was all based on fixed operating conditions and the fixed gear health state, which is a in-set detection, However, in real industrial scenarios, gear pitting fault diagnosis is always an open-set detection, in which the working conditions and the gear health state are commonly not known in advance. In order to deal with this open-set detection problem, we proposed a three-stage diagnosis method. In the first stage, we built an in-set health state classification model based on Domain2Vec to solve the domain generalization problem caused by different operating conditions. In the second stage, we modify the classification model to a regression model to predict the out-of-set health state sample in the dataset. In the third stage, we used KNN algorithm to correct the wrong model in the second stage and further improve the accuracy of classification. Our proposed method achieved scores of 463.5 and 472 on the test set and validation set, respectively, and ranked first in the 2023 PHM Conference Data Chanllenge.","PeriodicalId":91951,"journal":{"name":"Proceedings of the Annual Conference of the Prognostics and Health Management Society. Prognostics and Health Management Society. Conference","volume":"32 8","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134907962","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":"Fusion and Comparison of Prognostic Models for Remaining Useful Life of Aircraft systems","authors":"Shuai Fu, Nicolas P. Avdelidis, Angelos Plastropoulos, Ip-Shing Fan","doi":"10.36001/phmconf.2023.v15i1.3505","DOIUrl":"https://doi.org/10.36001/phmconf.2023.v15i1.3505","url":null,"abstract":"Changes in the performance of an aircraft system will straightforwardly affect the safe operation of the aircraft, and the technical requirements of Prognostics and Health Management (PHM) are highly relevant. Remaining Useful Life (RUL) prediction, part of the core technologies of PHM, is a cutting-edge innovation being worked on lately and an effective means to advance the change of upkeep support mode and work on the framework's security, unwavering quality, and economic reasonableness. This paper summarizes a detailed preliminary literature review and comparison of different prognostic approaches and the forecasting methods' taxonomy, the methodology's details, and provides its application to aircraft systems. It also provides a brief introduction to the predictive maintenance concept and condition-based maintenance (CBM). This article uses several predictive models to predict RUL and classifies conventional regression algorithms according to the similarity in function and form of the algorithms. More classical algorithms in each category are selected to compare the prediction results, and finally, the combined effects of the RUL prediction are obtained by weighted fusion, accuracy, and compatibility. The performance of the proposed models is assessed based on evaluations of RUL acquired from the hybrid and individual predictive models. This correlation depends on the most current prognostic metrics. The outcomes show that the proposed strategy develops precision, robustness, and adaptability. Hence, the work in this paper shall enrich the advancement of predictive maintenance and modern innovation of prognostic development.","PeriodicalId":91951,"journal":{"name":"Proceedings of the Annual Conference of the Prognostics and Health Management Society. Prognostics and Health Management Society. Conference","volume":"24 8","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134907761","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":"Case Study Comparing ROC and PRC Curves for Imbalanced Data","authors":"Dan Watson, Karl Reichard, Aaron Isaacson","doi":"10.36001/phmconf.2023.v15i1.3479","DOIUrl":"https://doi.org/10.36001/phmconf.2023.v15i1.3479","url":null,"abstract":"Receiver operating characteristic curves are a mainstay in binary classification and have seen widespread use from their inception characterizing radar receivers in 1941. Widely used and accepted, the ROC curve is the default option for many application spaces. Building on prior work the Prognostics and Health Management community naturally adopted ROC curves to visualize classifier performance. While the ROC curve is perhaps the best known visualization of binary classifier performance it is not the only game in town. Authors from across various STEM fields have published works extolling various other metrics and visualizations in binary classifier performance evaluation. These include, but are not limited to, the precision recall characteristic curve, area under the curve metrics, bookmaker informedness and markedness. This paper will review these visualizations and metrics, provide references for more exhaustive treatments on them, and provide a case study of their use on an imbalanced prognostic health management data-set. Prognostic health management binary classification problems are often highly imbalanced with a low prevalence of positive (faulty) cases compared to negative (nominal/healthy) cases. In the presented data-set, time domain accelerometer data for a series of run-to-failure ball-on-disk scuffing tests provide a case where the vast majority of data, > 94%, is from nominally healthy data instances. A condition indicator algorithm targeting the hypothesized physical system response is validated compared to less informed classifiers. Several characteristic curves are then used to showcase the performance improvement of the physics informed condition indicator.","PeriodicalId":91951,"journal":{"name":"Proceedings of the Annual Conference of the Prognostics and Health Management Society. Prognostics and Health Management Society. Conference","volume":"29 6","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134907232","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":"Vehicle State Monitoring and Fault Detection System for Unmanned Ground Vehicles (UGV) using Markov Models","authors":"Kalpit Vadnerkar, Pierluigi Pisu","doi":"10.36001/phmconf.2023.v15i1.3539","DOIUrl":"https://doi.org/10.36001/phmconf.2023.v15i1.3539","url":null,"abstract":"This research presents a novel fault detection and diagnostics system for unmanned ground vehicles (UGVs) by combining Markov models representing the vehicle's navigation, kinematic behavior, and vehicle dynamics systems. Existing studies do not specifically address the challenges related to UGVs and their complex subsystems or the incorporation of weather and environmental condition data. The proposed system leverages environmental and weather condition data to monitor the UGV's state and detect anomalies in its behavior. By predicting the probability of faults such as collisions, sensor damage, and other malfunctions, the system aims to enhance the safety, reliability, and performance of UGVs. The research will demonstrate the effectiveness of the proposed methodology through case studies and performance evaluation, highlighting its potential application in various real-world scenarios. This work contributes to the ongoing research in prognostics and health management, particularly for autonomous systems, by providing a new approach to fault detection and diagnostics in UGVs.","PeriodicalId":91951,"journal":{"name":"Proceedings of the Annual Conference of the Prognostics and Health Management Society. Prognostics and Health Management Society. Conference","volume":"18 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":"134907233","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":"Context-aware machine learning for estimating the remaining useful life of bearings under varying speed operating conditions","authors":"Seyed Ali Hosseinli, Ted Ooijevaar, Konstantinos Gryllias","doi":"10.36001/phmconf.2023.v15i1.3571","DOIUrl":"https://doi.org/10.36001/phmconf.2023.v15i1.3571","url":null,"abstract":"Remaining useful life estimation is a crucial and complicated task in predictive maintenance in order to reduce downtime and avoid catastrophic breakdowns in industrial plants. Thanks to the recent advances in our machine learning era, deep learning models can effectively deal with modeling complex phenomena such as the bearing degradation process, specifically under varying operating conditions. However, obtaining large labeled datasets for training the data-dependent deep learning models is challenging and expensive. To overcome this limitation, a phenomenological model has been used in this study as an effective approach to creating synthetic run-to-failure datasets under varying operating conditions. The suggested methodology is able to adjust synthetic run-to-failure datasets to the different periodic speed profiles, including the speed ranges that pass the resonance frequency of the structure. A Context-aware Domain Adversarial Neural Network is proposed to remove the domain shift between the simulated signals and the real ones as well as enable the deep learning model to understand the varying speed operating conditions and the sequential order of the measurements. The simulated signals are used as the source domain and a limited number of the real signals are used as the unlabeled samples for the domain adaptation task. Context awareness is introduced to the network by integrating contextual information into the architecture of the Domain Adversarial Neural Network, leading to an improvement in the model performance and its generalization ability. A dataset captured in a bearing test rig is adopted to verify the proposed method. Results show that context awareness can result in better performance and also more robust predictions against major speed changes in varying speed scenarios compared to the non-context-aware models.","PeriodicalId":91951,"journal":{"name":"Proceedings of the Annual Conference of the Prognostics and Health Management Society. Prognostics and Health Management Society. Conference","volume":"164 6","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134907246","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":"Fault Severity Estimation in Cracked Shafts by Integration of Phase Space Topology and Convolutional Neural Network","authors":"Utkarsh Andharikar, Amirhassan Abbasi, Prashant Kambali, C. Nataraj","doi":"10.36001/phmconf.2023.v15i1.3574","DOIUrl":"https://doi.org/10.36001/phmconf.2023.v15i1.3574","url":null,"abstract":"With the rapid advancement of industrial systems and the unavoidable complications and interconnectedness in systems, diagnostics of industrial machinery are achieving paramount importance. Accurate estimation of health condition of industrial machinery becomes more challenging due to the inherent nonlinearity, complexity, and uncertainty of the observations. Nonlinear dynamic analysis has proven to be a powerful tool for providing information about the health condition of a system that can be used for diagnostic applications. The current study particularly focuses on crack depth estimation using phase space analysis. Phase space provides a topological representation of the dynamics of the system and is highly informative about the health condition. The information suitable for diagnostics is employed by Convolutional Neural Networks, which are known to be powerful in extracting spatial information from maps. The proposed diagnostic method is evaluated on a Jeffcott rotor model with transverse crack in the rotating shaft to estimate the severity of the fault from the phase space topology as a case study.","PeriodicalId":91951,"journal":{"name":"Proceedings of the Annual Conference of the Prognostics and Health Management Society. Prognostics and Health Management Society. Conference","volume":"31 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":"134907587","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}