An integrated deep learning-based approach for automobile maintenance prediction with GIS data

Chen, Chong; Liu, Ying; Sun, Xianfang; Cairano-Gilfedder, Carla Di; Titmus, Scott

doi:10.1016/j.ress.2021.107919

Cited by 28 publications

(13 citation statements)

References 39 publications

(47 reference statements)

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“…As they are written by humans, the unstructured, nosily text is treated with natural language processing in successive stages to perform the PdM task. A similar approach, but applied to vehicles, is followed in Chen, Liu, et al (2021): instead of using sensor connectivity, it combines both maintenance records and weather, traffic and terrain factors, captured by the geographical information system of the vehicles fleet. In some works (Calabrese et al, 2020; Dangut et al, 2021; Koca et al, 2020; Pezze et al, 2021), the use of alarms or warnings designed for human interaction is proposed as a cheaper alternative to the implantation of sensors.…”

Section: Data Mining In Predictive Maintenancementioning

confidence: 99%

“…RNNs were introduced in Section 4.4.1, since they are able to perform classification or regression tasks depending on the configuration of the output layer. Regression‐based LSTM to predict RUL can be found in Zhang, Wang, et al (2018), Zschech et al (2019), Chen, Liu, et al (2021), and Xiong et al (2021). Two decision support systems are proposed in Lepenioti et al (2020) and Chen, Zhu, et al (2021) based on the RUL estimation produced by LSTM, among other components.…”

Section: Data Mining In Predictive Maintenancementioning

confidence: 99%

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Data mining in predictive maintenance systems: A taxonomy and systematic review

Esteban

Zafra

Ventura

2022

WIREs Data Min & Knowl

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Predictive maintenance is a field of study whose main objective is to optimize the timing and type of maintenance to perform on various industrial systems. This aim involves maximizing the availability time of the monitored system and minimizing the number of resources used in maintenance. Predictive maintenance is currently undergoing a revolution thanks to advances in industrial systems monitoring within the Industry 4.0 paradigm. Likewise, advances in artificial intelligence and data mining allow the processing of a great amount of data to provide more accurate and advanced predictive models. In this context, many actors have become interested in predictive maintenance research, becoming one of the most active areas of research in computing, where academia and industry converge. The objective of this paper is to conduct a systematic literature review that provides an overview of the current state of research concerning predictive maintenance from a data mining perspective. The review presents a first taxonomy that implies different phases considered in any data mining process to solve a predictive maintenance problem, relating the predictive maintenance tasks with the main data mining tasks to solve them. Finally, the paper presents significant challenges and future research directions in terms of the potential of data mining applied to predictive maintenance. This article is categorized under: Application Areas > Industry Specific Applications Technologies > Internet of Things

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Section: Data Mining In Predictive Maintenancementioning

confidence: 99%

Section: Data Mining In Predictive Maintenancementioning

confidence: 99%

Data mining in predictive maintenance systems: A taxonomy and systematic review

Esteban

Zafra

Ventura

2022

WIREs Data Min & Knowl

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“…It has been demonstrated that the proposed method is feasible and has a higher prediction accuracy than recurrent neural network (RNN) and support vector regression (SVR) [25]. Based on the historical maintenance data and GIS data, Chen et al [26] proposed a merged-LSTM network for the RUL prediction of the automobile. The RUL can be predicted from the service times of opening and closing switches collected by electrical equipment.…”

Section: Machine Learning For Predictive Maintenancementioning

confidence: 99%

A deep learning-based approach for electrical equipment remaining useful life prediction

Liu

2022

Auton. Intell. Syst.

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Electrical equipment maintenance is of vital importance to management companies. Efficient maintenance can significantly reduce business costs and avoid safety accidents caused by catastrophic equipment failures. In the current context, predictive maintenance (PdM) is becoming increasingly popular based on machine learning approaches, while its research on electrical equipment such as low-voltage contactors is in its infancy. The failure modes are mainly fusion welding and explosion, and a few are unable to switch on. In this study, a data-driven approach is proposed to predict the remaining useful life (RUL) of the low-voltage contactor. Firstly, the three-phase alternating voltage and current records the life of electrical equipment by tracking the number of times it has been operated. Secondly, the failure-relevant features are extracted by using the time domain, frequency domain, and wavelet methods. Then, a CNN-LSTM network is designed and used to train an electrical equipment RUL prediction model based on the extracted features. An experimental study based on ten datasets collected from low-voltage AC contactors reveals that the proposed method shows merits in comparison with the prevailing deep learning algorithms in terms of MAE and RMSE.

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“…Gu et al [24] proposed a multi-sensor fault diagnosis model based on long and short-term memory network (LSTM), which used the LSTM network to extract detailed fault information in frequency domain features after wavelet transformation for fault analysis. Chen et al [25] proposed an integrated model to predict the automobile failure time using a merged-LSTM algorithm. In the field of gearbox fault diagnosis, Shi et al [26] proposed a long and short-term memory network model based on two-way convolution to automatically extract the space-time characteristics of vibration and speed data, so as to detect gearbox fault categories.…”

Section: Literature Reviewmentioning

confidence: 99%

A Machine Learning-Based Approach for Elevator Door System Fault Diagnosis

Liang

Chen

Wang

et al. 2022

2022 IEEE 18th International Conference on Automation Science and Engineering (CASE)

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The door system is the core part of the elevator. An accurate diagnosis of the door system can aid engineers in troubleshooting and reduce maintenance costs. However, the research of fault diagnosis based on elevator operation and maintenance data is still in its infancy. With the development of the industrial Internet-of-things, real-time monitoring data of elevator can be collected and used for fault diagnosis modeling. This paper investigates a machine learning-based approach to achieve accurate elevator door fault diagnosis. An experimental study was conducted based on the monitoring data collected from the real-world elevator door system. The experimental results revealed that XGBoost algorithm can accurately identify the fault type of the elevator door.

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An integrated deep learning-based approach for automobile maintenance prediction with GIS data

Cited by 28 publications

References 39 publications

Data mining in predictive maintenance systems: A taxonomy and systematic review

Data mining in predictive maintenance systems: A taxonomy and systematic review

A deep learning-based approach for electrical equipment remaining useful life prediction

A Machine Learning-Based Approach for Elevator Door System Fault Diagnosis

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