Data-driven strategies for predictive maintenance: Lesson learned from an automotive use case

Giordano, Danilo; Giobergia, Flavio; Pastor, Eliana; Macchia, Antonio La; Cerquitelli, Tania; Baralis, Elena; Mellia, Marco; Tricarico, Davide

doi:10.1016/j.compind.2021.103554

Cited by 20 publications

(6 citation statements)

References 39 publications

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“…Categorical segmentation divided the data based on discrete categories or classes [11]. To ensure the reliability, the resulting segments were evaluated and validated using techniques such as cross-validation [12].…”

Section: Data Segmentationmentioning

confidence: 99%

Enhancing Gearbox Fault Diagnosis through Advanced Feature Engineering and Data Segmentation Techniques

Shukla,

Holderbaum,

Theodoridis

et al. 2024

Machines

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Efficient gearbox fault diagnosis is crucial for the cost-effective maintenance and reliable operation of rotating machinery. Despite extensive research, effective fault diagnosis remains challenging due to the multitude of features available for classification. Traditional feature selection methods often fail to achieve optimal performance in fault classification tasks. This study introduces diverse ranking methods for selecting the relevant features and utilizes data segmentation techniques such as sliding, windowing, and bootstrapping to strengthen predictive model performance and scalability. A comparative analysis of these methods was conducted to identify the potential causes and future solutions. An evaluation of the impact of enhanced feature engineering and data segmentation on predictive maintenance in gearboxes revealed promising outcomes, with decision trees, SVM, and KNN models outperforming others. Additionally, within a fully connected network, windowing emerged as a more robust and efficient segmentation method compared to bootstrapping. Further research is necessary to assess the performance of these techniques across diverse datasets and applications, offering comprehensive insights for future studies in fault diagnosis and predictive maintenance.

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Section: Data Segmentationmentioning

confidence: 99%

Enhancing Gearbox Fault Diagnosis through Advanced Feature Engineering and Data Segmentation Techniques

Shukla,

Holderbaum,

Theodoridis

et al. 2024

Machines

View full text Add to dashboard Cite

show abstract

“…[184][185][186][187][188][189] use the semantic web rule language, rule-based expert systems, fuzzy systems, quantitative association rule mining (QARM), and data-driven sensitivity analysis. In [190][191][192][193][194][195][196][197][198][199][200][201][202][203][204], some techniques and visualizations such as decision trees, graph-based approaches, the attention modules used together with LSTM, generative adversarial networks (GAN), PDPs, and feature importance calculation methods. Refs.…”

Section: Transparency and Explainability In Ai-based Predictive Maint...mentioning

confidence: 99%

“…Pobability density function, Fourier transform, spectral kurtosis, autoencoder and variational autoencoder, and K-means clustering Gearbox and bearing health assessment in wind turbine system [204] Unsupervised feature selection, adapting relevance metrics with the dynamic time-warping algorithm Health indicators for a rotating machine [205] Temporal fusion separable convolutional network, a hierarchical latent space variational auto-encoder, and a regressor consisting of a linear layer and a sigmoid activation function RUL estimation on the NASA turbofan engine dataset [132,133] [206] A blockchain-based architecture that achieves trustworthy federated learning A service [207] Balanced K-star PdM in an IoT-based manufacturing system [208] HMM and reinforcement learning RUL estimation of the engines on the NASA turbofan engine dataset [132,133] An extensive application range of PdM consists of a coal crusher operating at the boiler of the real power plant and gantries in a steelworks converter, a transport line in a steelworks converter, the MW load range in a petrochemical plant, a real hybrid bus, hydraulic systems, a modular aero-propulsion system, an intelligent manufacturing system, the water pumping industry, a large gas distribution network, rolling bearings and a rotating machine, PdM for autonomous underwater vehicles (AUV), a water injection pump, wind turbine systems, the IoT-based manufacturing environment, hard disk drives, the turbofan engine, the drilling machine of an automotive manufacturer, solar photovoltaic energy systems, maintenance work orders, manufacturing, and structural health monitoring.…”

Section: Refmentioning

confidence: 99%

Artificial Intelligence for Predictive Maintenance Applications: Key Components, Trustworthiness, and Future Trends

Ucar,

Karakose,

Kırımça

2024

Applied Sciences

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Predictive maintenance (PdM) is a policy applying data and analytics to predict when one of the components in a real system has been destroyed, and some anomalies appear so that maintenance can be performed before a breakdown takes place. Using cutting-edge technologies like data analytics and artificial intelligence (AI) enhances the performance and accuracy of predictive maintenance systems and increases their autonomy and adaptability in complex and dynamic working environments. This paper reviews the recent developments in AI-based PdM, focusing on key components, trustworthiness, and future trends. The state-of-the-art (SOTA) techniques, challenges, and opportunities associated with AI-based PdM are first analyzed. The integration of AI technologies into PdM in real-world applications, the human–robot interaction, the ethical issues emerging from using AI, and the testing and validation abilities of the developed policies are later discussed. This study exhibits the potential working areas for future research, such as digital twin, metaverse, generative AI, collaborative robots (cobots), blockchain technology, trustworthy AI, and Industrial Internet of Things (IIoT), utilizing a comprehensive survey of the current SOTA techniques, opportunities, and challenges allied with AI-based PdM.

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“…Big Data analysis facilitates PM’s real-time decision-making using data-driven analytical tools (Subramaniyan et al , 2018). Giordano et al (2022) applied data-driven strategies for the PdM of automobiles. Moreover, big data analysis will aid in the logistical management of spare parts, which is crucial in maintenance (Hazen et al , 2014).…”

Section: Introductionmentioning

confidence: 99%

Analysis of maintenance outsourcing challenges for belt conveyors in the Industry 4.0 era

Parmar

James

Asjad

2023

JGOSS

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Purpose There is an increasing trend of outsourcing maintenance activities of heavy equipment, including belt conveyor installations. However, there are numerous challenges in maintenance outsourcing. This paper aims to identify and analyze various challenges of outsourcing maintenance activities associated with belt conveyor installations. Design/methodology/approach This paper identifies maintenance outsourcing challenges of belt conveyor installations through literature review, field visits and expert opinion. An integrated structural hierarchical framework of the identified challenges is developed through analytic hierarchy process and decision-making trial and evaluation laboratory. Findings The paper has identified eight challenges, namely, attainment of organizational strength by contractors, legal and financial challenges for contractors, attainment of necessary technician skills by contractors, maintenance data acquisition and analysis challenges, facilitation with modern equipment, gadgets and instrumentation, service quality challenges, health, safety and environment-related challenges and spares supply chain management challenges. The segregation of driver and dependent challenges, including their hierarchical framework had been established in this work. Research limitations/implications A comprehensive list of challenges and their prioritization in maintenance outsourcing of belt conveyor installations had been established. This will help the organizations who own and operate these installations to make judicious decisions regarding outsourcing maintenance. Originality/value This paper significantly contributes to the literature on maintenance outsourcing of heavy machinery installations like a belt conveyor system based on the input of different stakeholders. This study will lead to the development of frameworks for maintenance contractor selection for such installations.

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Data-driven strategies for predictive maintenance: Lesson learned from an automotive use case

Cited by 20 publications

References 39 publications

Enhancing Gearbox Fault Diagnosis through Advanced Feature Engineering and Data Segmentation Techniques

Enhancing Gearbox Fault Diagnosis through Advanced Feature Engineering and Data Segmentation Techniques

Artificial Intelligence for Predictive Maintenance Applications: Key Components, Trustworthiness, and Future Trends

Analysis of maintenance outsourcing challenges for belt conveyors in the Industry 4.0 era

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