Mathematics

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19 pages, 8913 KiB

Open AccessArticle

Analysis of Production and Failure Data in Automotive: From Raw Data to Predictive Modeling and Spare Parts

by Cristiano Fragassa

Mathematics 2024, 12(4), 510; https://doi.org/10.3390/math12040510 - 06 Feb 2024

Viewed by 731

The present analysis examines extensive and consistent data from automotive production and service to assess reliability and predict failures in the case of an engine control device. It is based on statistical evaluation of production and lead times to determine vehicle sales. Mileages [...] Read more.

The present analysis examines extensive and consistent data from automotive production and service to assess reliability and predict failures in the case of an engine control device. It is based on statistical evaluation of production and lead times to determine vehicle sales. Mileages are integrated to establish the age of the vehicle fleet over time and to predict the censored data. Failure and censored times are merged in a multiple censored data and combined by the Kaplan-Meier estimator for survivals. The Weibull distribution is used as parametric reliability model and its parameters identified to assure precision in predictions (>95%). An average time to failure >80 years and a slightly increasing failure rate ensure a low risk. The study is based on real-world data from various sources, acknowledging that the data are not homogeneous, and it offers a comprehensive roadmap for processing this diverse raw data and evolving it into sophisticated predictive models. Furthermore, it provides insights from various perspectives, including those of the Original Equipment Manufacturer, Car Manufacturer, and Users. Full article

(This article belongs to the Special Issue Data-Driven Methods and Artificial Intelligence in Reliability and Maintenance, 2nd Edition)

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30 pages, 8374 KiB

Open AccessArticle

Benchmarking Maintenance Practices for Allocating Features Affecting Hydraulic System Maintenance: A West-Balkan Perspective

by Marko Orošnjak and Dragoljub Šević

Mathematics 2023, 11(18), 3816; https://doi.org/10.3390/math11183816 - 05 Sep 2023

Cited by 3 | Viewed by 814

Abstract

As a consequence of the application advanced maintenance practices, the theoretical probability of failures occurring is relatively low. However, observations of low levels of market intelligence and maintenance management have been reported. This comprehensive study investigates the determinants of maintenance practices in companies [...] Read more.

As a consequence of the application advanced maintenance practices, the theoretical probability of failures occurring is relatively low. However, observations of low levels of market intelligence and maintenance management have been reported. This comprehensive study investigates the determinants of maintenance practices in companies utilising hydraulic machinery, drawing on empirical evidence from a longitudinal questionnaire-based survey across the West-Balkan countries. This research identifies critical predictors of technical and sustainable maintenance performance metrics by employing the CA-AHC (Correspondence Analysis with Agglomerative Hierarchical Clustering) method combined with non-parametric machine learning models. Key findings highlight the significant roles of the number of maintenance personnel employed; equipment size, determined on the basis of nominal power consumption; machinery age; and maintenance activities associated with fluid cleanliness in influencing hydraulic machine maintenance outcomes. These insights challenge current perceptions and introduce novel considerations with respect to aspects such as equipment size, maintenance skills and activities with the aim of preserving peak performance. However, the study acknowledges the variability resulting from differing operational conditions, and calls for further research for broader validation. As large-scale heterogeneous datasets are becoming mainstream, this research underscores the importance of using multidimensional data analysis techniques to better understand operational outcomes. Full article

(This article belongs to the Special Issue Data-Driven Methods and Artificial Intelligence in Reliability and Maintenance, 2nd Edition)

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19 pages, 2909 KiB

Open AccessArticle

An Optimal Opportunistic Maintenance Planning Integrating Discrete- and Continuous-State Information

by Fanping Wei, Jingjing Wang, Xiaobing Ma, Li Yang and Qingan Qiu

Mathematics 2023, 11(15), 3322; https://doi.org/10.3390/math11153322 - 28 Jul 2023

Viewed by 797

Abstract

Information-driven group maintenance is crucial to enhance the operational availability and profitability of diverse industrial systems. Existing group maintenance models have primarily concentrated on a single health criterion upon maintenance implementation, where the fusion of multiple health criteria is rarely reported. However, this [...] Read more.

Information-driven group maintenance is crucial to enhance the operational availability and profitability of diverse industrial systems. Existing group maintenance models have primarily concentrated on a single health criterion upon maintenance implementation, where the fusion of multiple health criteria is rarely reported. However, this is not aligned with actual maintenance planning of multi-component systems on many occasions, where multi-source health information can be integrated to support robust decision making. Additionally, how to improve maintenance effectiveness through a scientific union of both scheduled and unscheduled maintenance remains a challenge in group maintenance. This study addresses these research gaps by devising an innovative multiple-information-driven group replacement policy for serial systems. In contrast to existing studies, both discrete-state information (hidden defect) and continuous degradation information are employed for group maintenance planning, and scheduled postponed maintenance and unscheduled opportunistic maintenance are dynamically integrated for the first time to mitigate downtime loss. To be specific, inspections are equally spaced to reveal system health states, followed by the multi-level replacement implemented when either (a) the degradation of the continuously degrading unit reaches a specified threshold, or (b) the age of the multi-state unit since the defect’s identification reaches a pre-set age (delayed replacement). Such scheduling further enables the implementation of multi-source opportunistic replacement to alleviate downtime. The Semi-Markov Decision Process (SMDP) is utilized for the collaborative optimization of continuous- and discrete-state thresholds, so as to minimize the operational costs. Numerical experiments conducted on the critical structure of circulating pumps verify the model’s applicability. Full article

(This article belongs to the Special Issue Data-Driven Methods and Artificial Intelligence in Reliability and Maintenance, 2nd Edition)

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23 pages, 8502 KiB

Open AccessArticle

Long-Term Lifetime Prediction of Power MOSFET Devices Based on LSTM and GRU Algorithms

by Mesfin Seid Ibrahim, Waseem Abbas, Muhammad Waseem, Chang Lu, Hiu Hung Lee, Jiajie Fan and Ka-Hong Loo

Mathematics 2023, 11(15), 3283; https://doi.org/10.3390/math11153283 - 26 Jul 2023

Cited by 1 | Viewed by 1695

Abstract

Predicting the long-term lifetime of power MOSFET devices plays a central role in the prevention of unprecedented failures for power MOSFETs used in safety-critical applications. The various traditional model-based approaches and statistical and filtering algorithms for prognostics have limitations in terms of handling [...] Read more.

Predicting the long-term lifetime of power MOSFET devices plays a central role in the prevention of unprecedented failures for power MOSFETs used in safety-critical applications. The various traditional model-based approaches and statistical and filtering algorithms for prognostics have limitations in terms of handling the dynamic nature of failure precursor degradation data for these devices. In this paper, a prognostic model based on LSTM and GRU is developed that aims at estimating the long-term lifetime of discrete power MOSFETs using dominant failure precursor degradation data. An accelerated power cycling test has been designed and executed to collect failure precursor data. For this purpose, commercially available power MOSFETs passed through power cycling tests at different temperature swing conditions and potential failure precursor data were collected using an automated curve tracer after certain intervals. The on-state resistance degradation data identified as one of the dominant failure precursors and potential aging precursors has been analyzed using RNN, LSTM, and GRU-based algorithms. The LSTM and GRU models have been found to be superior compared to RNN, with MAPE of 0.9%, 0.78%, and 1.72% for MOSFET 1; 0.90%, 0.66%, and 0.6% for MOSFET 5; and 1.05%, 0.9%, and 0.78%, for MOSFET 9, respectively, predicted at 40,000 cycles. In addition, the robustness of these methods is examined using training data at 24,000 and 54,000 cycles of starting points and is able to predict the long-term lifetime accurately, as evaluated by MAPE, MSE, and RMSE metrics. In general, the prediction results showed that the prognostics algorithms developed were trained to provide effective, accurate, and useful lifetime predictions and were found to address the reliability concerns of power MOSFET devices for practical applications. Full article

(This article belongs to the Special Issue Data-Driven Methods and Artificial Intelligence in Reliability and Maintenance, 2nd Edition)

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18 pages, 1115 KiB

Open AccessArticle

Functional Subspace Variational Autoencoder for Domain-Adaptive Fault Diagnosis

by Tan Li, Che-Heng Fung, Him-Ting Wong, Tak-Lam Chan and Haibo Hu

Mathematics 2023, 11(13), 2910; https://doi.org/10.3390/math11132910 - 28 Jun 2023

Viewed by 920

Abstract

This paper presents the functional subspace variational autoencoder, a technique addressing challenges in sensor data analysis in transportation systems, notably the misalignment of time series data and a lack of labeled data. Our technique converts vectorial data into functional data, which captures continuous [...] Read more.

This paper presents the functional subspace variational autoencoder, a technique addressing challenges in sensor data analysis in transportation systems, notably the misalignment of time series data and a lack of labeled data. Our technique converts vectorial data into functional data, which captures continuous temporal dynamics instead of discrete data that consist of separate observations. This conversion reduces data dimensions for machine learning tasks in fault diagnosis and facilitates the efficient removal of misalignment. The variational autoencoder identifies trends and anomalies in the data and employs a domain adaptation method to associate learned representations between labeled and unlabeled datasets. We validate the technique’s effectiveness using synthetic and real-world transportation data, providing valuable insights for transportation infrastructure reliability monitoring. Full article

(This article belongs to the Special Issue Data-Driven Methods and Artificial Intelligence in Reliability and Maintenance, 2nd Edition)

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16 pages, 4726 KiB

Open AccessArticle

Non-Ferrous Metal Price Point and Interval Prediction Based on Variational Mode Decomposition and Optimized LSTM Network

by Yifei Zhao, Jianhong Chen, Hideki Shimada and Takashi Sasaoka

Mathematics 2023, 11(12), 2738; https://doi.org/10.3390/math11122738 - 16 Jun 2023

Cited by 2 | Viewed by 1057

Abstract

The accurate forecasting of metal prices is of great importance to industrial producers as the supply of metal raw materials is a very important part of industrial production. The futures market is subject to many factors, and metal prices are highly volatile. In [...] Read more.

The accurate forecasting of metal prices is of great importance to industrial producers as the supply of metal raw materials is a very important part of industrial production. The futures market is subject to many factors, and metal prices are highly volatile. In the past, most of the relevant research has focused only on deterministic point forecasting, with less research performed on interval uncertainty forecasting. Therefore, this paper proposes a novel forecasting model that combines point forecasting and interval forecasting. First, a novel hybrid price point forecasting model was established using Variational Modal Decomposition (VMD) and a Long Short-Term Memory Neural Network (LSTM) based on Sparrow Search Algorithm (SSA) optimization. Then, five distribution functions based on the optimization algorithm were used to fit the time series data patterns and analyze the metal price characteristics, Finally, based on the optimal distribution function and point forecasting results, the forecasting range and confidence level were set to determine the interval forecasting model. The interval forecasting model was validated by inputting the price data of copper and aluminum into the model and obtaining the interval forecasting results. The validation results show that the proposed hybrid forecasting model not only outperforms other comparative models in terms of forecasting accuracy, but also has a better performance in forecasting sharp fluctuations and data peaks, which can provide a more valuable reference for producers and investors. Full article

(This article belongs to the Special Issue Data-Driven Methods and Artificial Intelligence in Reliability and Maintenance, 2nd Edition)

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21 pages, 3451 KiB

Open AccessArticle

Carbon Trading Price Prediction of Three Carbon Trading Markets in China Based on a Hybrid Model Combining CEEMDAN, SE, ISSA, and MKELM

by Haoran Zhao and Sen Guo

Mathematics 2023, 11(10), 2319; https://doi.org/10.3390/math11102319 - 16 May 2023

Viewed by 1050

Abstract

Carbon trading has been deemed as the most effective mechanism to mitigate carbon emissions. However, during carbon trading market operation, competition among market participants will inevitably occur; hence, the precise forecasting of the carbon trading price (CTP) has become a significant element in [...] Read more.

Carbon trading has been deemed as the most effective mechanism to mitigate carbon emissions. However, during carbon trading market operation, competition among market participants will inevitably occur; hence, the precise forecasting of the carbon trading price (CTP) has become a significant element in the formulation of competition strategies. This investigation has established a hybrid CTP forecasting framework combining complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), sample entropy (SE) method, improved salp swarm algorithm (ISSA), and multi-kernel extreme learning machine (MKELM) methods to improve forecasting accuracy. Firstly, the initial CTP data sequence is disintegrated into several intrinsic mode functions (IMFs) and a residual sequence by a CEEMDAN method. Secondly, to save calculation time, SE method has been utilized to reconstruct the IMFs and the residual sequence into new IMFs. Thirdly, the new IMFs are fed into the MKELM model, combing RBF and the poly kernel functions to utilize their superior learning and generalization abilities. The parameters of the MKELM model are optimized by ISSA, combining dynamic inertia weight and chaotic local searching method into the SSA to enhance the searching speed, convergence precision, as well as the global searching ability. CTP data in Guangdong, Shanghai, and Hubei are selected to prove the validity of the established CEEMDAN-SE-ISSA-MKELM model. Through a comparison analysis, the established CEEMDAN-SE-ISSA-MKELM model performs the best with the smallest MAPE and RMSE values and the highest R2 value, which are 0.76%, 0.53, and 0.99, respectively, for Guangdong,. Thus, the presented model would be extensively applied in CTP forecasting in the future. Full article

(This article belongs to the Special Issue Data-Driven Methods and Artificial Intelligence in Reliability and Maintenance, 2nd Edition)

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17 pages, 6303 KiB

Open AccessArticle

Prediction of Tool Remaining Useful Life Based on NHPP-WPHM

by Yingzhi Zhang, Guiming Guo, Fang Yang, Yubin Zheng and Fenli Zhai

Mathematics 2023, 11(8), 1837; https://doi.org/10.3390/math11081837 - 12 Apr 2023

Cited by 3 | Viewed by 1076

Abstract

A tool remaining useful life prediction method based on a non-homogeneous Poisson process and Weibull proportional hazard model (WPHM) is proposed, taking into account the grinding repair of machine tools during operation. The intrinsic failure rate model is built according to the tool [...] Read more.

A tool remaining useful life prediction method based on a non-homogeneous Poisson process and Weibull proportional hazard model (WPHM) is proposed, taking into account the grinding repair of machine tools during operation. The intrinsic failure rate model is built according to the tool failure data. The WPHM is established by collecting vibration information during operation and introducing covariates to describe the failure rate of the tool operation. In combination with the tool grinding repair, the NHPP-WPHM under different repair times is established to describe the tool comprehensive failure rate. The failure threshold of the tool life is determined by the maximum availability, and the remaining tool life is predicted. Take the cylindrical turning tool of the CNC lathe as an example, the root mean square error, mean absolute error, mean absolute percentage error, and determination coefficient (R²) are used as indicators. The proposed method is compared with the actual remaining useful life and the remaining useful life prediction model based on the WPHM to verify the effectiveness of the model. Full article

(This article belongs to the Special Issue Data-Driven Methods and Artificial Intelligence in Reliability and Maintenance, 2nd Edition)

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