Simple continuous optimal regions of the space of data

Carrega, Alessio; Cipollini, Francesca; Oneto, Luca

doi:10.1016/j.neucom.2019.03.081

Cited by 4 publications

(4 citation statements)

References 18 publications

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“…Most of these systems are based on extracting key features and comparing the values of these features with threshold values to notify the occurrence of anomalous instances (Bastami and Vahid, 2021;Grzechca et al, 2021b). Statistical techniques are useful when anomalies emerge as outliers, and removing these JQME 29,2 outliers might improve the system's accuracy rather than processing the anomalies to extract any meaningful information (Heydari et al, 2021;Wang et al, 2020;Carrega et al, 2019). These statistical methods are applied in various industries like manufacturing (Ming et al, 2019), automobile (Bastami and Vahid, 2021), electrical (Guerrero et al, 2020) and aircraft maintenance systems (Pfingstl et al, 2020).…”

Section: Methodsmentioning

confidence: 99%

“…Combining multiple machine learning algorithms develops robust and accurate fault prediction techniques (Fernandes et al, 2019). (Ashok et al, 2021;Shaheen et al, 2021;Bastami and Vahid, 2021;Grzechca et al, 2021a, b;Heydari et al, 2021;Wang et al, 2020;Carrega et al, 2019;Guerrero et al, 2020;Pfingstl et al, 2020) A major difficulty in predicting faults is recognising unexpected failures and recurring trends (Manco et al, 2017). Programming efforts are underway to develop automated fault diagnostic systems and fault detection protocols (Sarda et al, 2021;Diaz et al, 2017;Schmidt et al, 2017).…”

Section: Domain-wise Classification Of Methodsmentioning

confidence: 99%

“…In a large production system, anomaly detection systems can be utilised to determine whether the current product shows an anomalous behaviour with respect to a defined nominal state (Carrega et al, 2019). Recently, cloud-based predictive maintenance based on mobile agents are used for timely fault diagnosis and maintenance scheduling (Calabrese et al, 2021).…”

Section: Fault Detection Techniquesmentioning

confidence: 99%

Section: Rq1: What Are the Domains That Use Anomaly Detection For Pre...mentioning

confidence: 99%

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Review of fault detection techniques for predictive maintenance

Divya

Bhasi

Kumar

2022

JQME

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PurposeThis study aims to bring awareness to the developing of fault detection systems using the data collected from sensor devices/physical devices of various systems for predictive maintenance. Opportunities and challenges in developing anomaly detection algorithms for predictive maintenance and unexplored areas in this context are also discussed.Design/methodology/approachFor conducting a systematic review on the state-of-the-art algorithms in fault detection for predictive maintenance, review papers from the years 2017–2021 available in the Scopus database were selected. A total of 93 papers were chosen. They are classified under electrical and electronics, civil and constructions, automobile, production and mechanical. In addition to this, the paper provides a detailed discussion of various fault-detection algorithms that can be categorised under supervised, semi-supervised, unsupervised learning and traditional statistical method along with an analysis of various forms of anomalies prevalent across different sectors of industry.FindingsBased on the literature reviewed, seven propositions with a focus on the following areas are presented: need for a uniform framework while scaling the number of sensors; the need for identification of erroneous parameters; why there is a need for new algorithms based on unsupervised and semi-supervised learning; the importance of ensemble learning and data fusion algorithms; the necessity of automatic fault diagnostic systems; concerns about multiple fault detection; and cost-effective fault detection. These propositions shed light on the unsolved issues of predictive maintenance using fault detection algorithms. A novel architecture based on the methodologies and propositions gives more clarity for the reader to further explore in this area.Originality/valuePapers for this study were selected from the Scopus database for predictive maintenance in the field of fault detection. Review papers published in this area deal only with methods used to detect anomalies, whereas this paper attempts to establish a link between different industrial domains and the methods used in each industry that uses fault detection for predictive maintenance.

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