Data-Driven Predictive Maintenance Approach for Spinning Cyber-Physical Production System

Farooq, Basit; Bao, Jinsong; Li, Jie; Liu, Tianyuan; Shi-yong, Yin

doi:10.1007/s12204-020-2178-z

Cited by 15 publications

(11 citation statements)

References 17 publications

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“…Table 6 depicts maintenance process characteristics from analysed papers. The condition-based preventive maintenance is discussed in Farooq et al [ 38 ], Kumar et al [ 102 ], Li et al [ 96 ], Lin et al [ 103 ], Musselman and Djurdjanovic [ 104 ], Yan et al [ 93 ], and Sadiki et al [ 105 ]. Preventive maintenance is regular maintenance of machines, devices, and equipment to prevent their downtime concerning failure state.…”

Section: Resultsmentioning

confidence: 99%

“…Enterprises use speedometers, accelerometers, light detection and ranging (LIDAR) sensors, tachometers, Doppler radars, etc. Farooq et al [ 38 ] discussed genetic-algorithm-based prediction process for intelligent maintenance of textile spinning systems. They used vibration and speed data in a multiagent system for tracking discrepancies and error distribution of machine processing.…”

Section: Resultsmentioning

confidence: 99%

“…Thanks to the current capacity of computer technology, even large volumes of data can be processed faster, which allows smart industry systems to move information in real-time. Farooq et al [ 38 ] consider industrial prognosis and applying predictive analytics using machine learning techniques as new challenges to identify failure modes and reduce downtime. The application of the simulation enhances more possibilities for after-market services, such as maintenance [ 92 ].…”

Section: Discussionmentioning

confidence: 99%

“…This approach is based on artificial intelligence, i.e., machine learning and statistical modelling, and is a satisfactory approach in the conditions of Industry 4.0 [ 37 ]. Farooq et al distinguish experience-driven and data-driven maintenance [ 38 ]. Experience-driven preventive maintenance is based on gathering knowledge about production equipment, which is then used to plan future maintenance.…”

Section: Theoretical Backgroundmentioning

confidence: 99%

“…Farooq et al [38] We established a genetic algorithm (GA) based on multi-sensor performance assessment and prediction procedure for the spinning system. We have successfully adopted a GA-based prediction process for our spinning system, which worked as an intelligent maintenance and scheduling system for health assessment.…”

Section: I4mentioning

confidence: 99%

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Predictive Maintenance and Intelligent Sensors in Smart Factory: Review

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With the arrival of new technologies in modern smart factories, automated predictive maintenance is also related to production robotisation. Intelligent sensors make it possible to obtain an ever-increasing amount of data, which must be analysed efficiently and effectively to support increasingly complex systems’ decision-making and management. The paper aims to review the current literature concerning predictive maintenance and intelligent sensors in smart factories. We focused on contemporary trends to provide an overview of future research challenges and classification. The paper used burst analysis, systematic review methodology, co-occurrence analysis of keywords, and cluster analysis. The results show the increasing number of papers related to key researched concepts. The importance of predictive maintenance is growing over time in relation to Industry 4.0 technologies. We proposed Smart and Intelligent Predictive Maintenance (SIPM) based on the full-text analysis of relevant papers. The paper’s main contribution is the summary and overview of current trends in intelligent sensors used for predictive maintenance in smart factories.

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