Review of fault detection techniques for predictive maintenance

Divya, D.; Bhasi, M.; Kumar, Mukesh

doi:10.1108/jqme-10-2020-0107

Cited by 31 publications

(11 citation statements)

References 107 publications

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“…Faults are of diverse types that are based on the equipment. Fault prediction can be done by using degradation models that assist in the remaining useful lifetime of the equipment [8]. Errors or failure history is used for fault analysis.…”

Section: A Predictive Maintenancementioning

confidence: 99%

A Systematic Mapping Study of Predictive Maintenance in SMEs

et al. 2022

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The rapid growth of Industry 4.0 and predictive methods fostered a great potential for state-of-the-art techniques in the industrial sector, especially in smart factories. The equipment failure or system breakdowns during run time of a factory creates a severe problems towards impoverishment of the production system and destitution of the business. Predictive Maintenance (PdM) is a cost-saving and data driven technique to predict the maintenance time of in-service equipment or systems to reduce breakdown time and increase productivity. Although PdM is pragmatically adopted in large-scale industries, there is a lack of studies that map the PdM adoption in small and medium-sized enterprises (SMEs). In this systematic mapping study (SMS), we focus on predictive maintenance from an SME perspective to explore the field for researchers, scientists, and developers to comprehend the potential of PdM systems, their challenges, distinctive characteristics, and best practices in SMEs. Our study is based on four research questions comprised of demographic data, key challenges, distinctive characteristics, and best practices of predictive maintenance in SMEs. We found that the current literature on PdM is deficient in the SME domain, especially the financial side is vague. There is a huge potential for PdM in SMEs to design cost models and focus on data availability impediments. Management and monitoring of PdM and skilled personnel are also inadequate. Thus, we present a study that extracts the knowledge from the existing literature about PdM in SMEs, finds the research gap, and can assist in identifying the barriers and challenges of PdM adoption in SMEs.

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Section: A Predictive Maintenancementioning

confidence: 99%

A Systematic Mapping Study of Predictive Maintenance in SMEs

et al. 2022

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“…In this context, an effective maintenance plan is the essential approach to lubricating system failure risk prevention (Yang, 2004). Maintenance is classified into three types: improvement maintenance (IM), preventive maintenance (PM) and corrective maintenance (CM) (Divya et al, 2022). IM aims at optimizing maintenance in the design stage, while PM seeks to predict failure to ensure the normal operation of equipment, and CM involves timely maintenance operations after failure.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A computer vision approach to improve maintenance automation for thermal power plants lubrication systems

Bao,

Fan,

et al. 2023

JQME

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PurposeLubricating oil leakage is a common issue in thermal power plant operation sites, requiring prompt equipment maintenance. The real-time detection of leakage occurrences could avoid disruptive consequences caused by the lack of timely maintenance. Currently, inspection operations are mostly carried out manually, resulting in time-consuming processes prone to health and safety hazards. To overcome such issues, this paper proposes a machine vision-based inspection system aimed at automating the oil leakage detection for improving the maintenance procedures.Design/methodology/approachThe approach aims at developing a novel modular-structured automatic inspection system. The image acquisition module collects digital images along a predefined inspection path using a dual-light (i.e. ultraviolet and blue light) illumination system, deploying the fluorescence of the lubricating oil while suppressing unwanted background noise. The image processing module is designed to detect the oil leakage within the digital images minimizing detection errors. A case study is reported to validate the industrial suitability of the proposed inspection system.FindingsOn-site experimental results demonstrate the capabilities to complete the automatic inspection procedures of the tested industrial equipment by achieving an oil leakage detection accuracy up to 99.13%.Practical implicationsThe proposed inspection system can be adopted in industrial context to detect lubricant leakage ensuring the equipment and the operators safety.Originality/valueThe proposed inspection system adopts a computer vision approach, which deploys the combination of two separate sources of light, to boost the detection capabilities, enabling the application for a variety of particularly hard-to-inspect industrial contexts.

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“…During this time, they are unavailable, resulting in disrupted production scheduling. Therefore, production scheduling and maintenance planning are inextricably linked, and it is essential in intelligent manufacturing to establish an integrated plan to keep machines in healthy working conditions while producing jobs (Cui et al, 2019;Divya et al, 2022;Liao et al, 2017). Although this integration has been studied for three decades, it requires attention to greenhouse gas (GHG) emissions and sustainability research (Li and Wang, 2022;Ma et al, 2010).…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, production scheduling and maintenance planning are inextricably linked, and it is essential in intelligent manufacturing to establish an integrated plan to keep machines in healthy working conditions while producing jobs (Cui et al. , 2019; Divya et al. , 2022; Liao et al.…”

Section: Introductionmentioning

confidence: 99%

Joint maintenance planning and production scheduling optimization model for green environment

Attia,

Alatwi,

Al Hanbali

et al. 2023

JQME

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PurposeThis research integrates maintenance planning and production scheduling from a green perspective to reduce the carbon footprint.Design/methodology/approachA mixed-integer nonlinear programming (MINLP) model is developed to study the relation between production makespan, energy consumption, maintenance actions and footprint, i.e. service level and sustainability measures. The speed scaling technique is used to control energy consumption, the capping policy is used to control CO2 footprint and preventive maintenance (PM) is used to keep the machine working in healthy conditions.FindingsIt was found that ignoring maintenance activities increases the schedule makespan by more than 21.80%, the total maintenance time required to keep the machine healthy by up to 75.33% and the CO2 footprint by 15%.Research limitations/implicationsThe proposed optimization model can simultaneously be used for maintenance planning, job scheduling and footprint minimization. Furthermore, it can be extended to consider other maintenance activities and production configurations, e.g. flow shop or job shop scheduling.Practical implicationsMaintenance planning, production scheduling and greenhouse gas (GHG) emissions are intertwined in the industry. The proposed model enhances the performance of the maintenance and production systems. Furthermore, it shows the value of conducting maintenance activities on the machine's availability and CO2 footprint.Originality/valueThis work contributes to the literature by combining maintenance planning, single-machine scheduling and environmental aspects in an integrated MINLP model. In addition, the model considers several practical features, such as machine-aging rate, speed scaling technique to control emissions, minimal repair (MR) and PM.

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Review of fault detection techniques for predictive maintenance

Cited by 31 publications

References 107 publications

A Systematic Mapping Study of Predictive Maintenance in SMEs

A Systematic Mapping Study of Predictive Maintenance in SMEs

A computer vision approach to improve maintenance automation for thermal power plants lubrication systems

Joint maintenance planning and production scheduling optimization model for green environment

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