A Review of Sustainable Maintenance Strategies for Single Component and Multicomponent Equipment

Zhao, J.; Gao, Chunhai; Tang, Tao

doi:10.3390/su14052992

Cited by 26 publications

(20 citation statements)

References 165 publications

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“…As mentioned earlier, all the methods were used to train on fused signals data. To assess the resulting models, multi-class confusion matrices were computed and Cohen's Kappa (K) was measured according to Equation (1). See the confusion matrices in Figure 11, where the column at the far right of the plot shows the precision, the row at the bottom of the plot shows the recall, and the cell at the bottom right of the plot shows the overall accuracy.…”

Section: Results Of Models Trained On Fused Sensor Datamentioning

confidence: 99%

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Modeling and Fault Detection of Brushless Direct Current Motor by Deep Learning Sensor Data Fusion

Fogou

Meisel

Jongmanns

et al. 2022

Sensors

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Only with new sensor concepts in a network, which go far beyond what the current state-of-the-art can offer, can current and future requirements for flexibility, safety, and security be met. The combination of data from many sensors allows a richer representation of the observed phenomenon, e.g., system degradation, which can facilitate analysis and decision-making processes. This work addresses the topic of predictive maintenance by exploiting sensor data fusion and artificial intelligence-based analysis. With a dataset such as vibration and sound from sensors, we focus on studying paradigms that orchestrate the most optimal combination of sensors with deep learning sensor fusion algorithms to enable predictive maintenance. In our experimental setup, we used raw data obtained from two sensors, a microphone, and an accelerometer installed on a brushless direct current (BLDC) motor. The data from each sensor were processed individually and, in a second step, merged to create a solid base for analysis. To diagnose BLDC motor faults, this work proposes to use data-level sensor fusion with deep learning methods such as deep convolutional neural networks (DCNNs) for their ability to automatically extract relevant information from the input data, the long short-term memory method (LSTM), and convolutional long short-term memory (CNN-LSTM), a combination of the two previous methods. The results show that in our setup, sound signals outperform vibrations when used individually for training. However, without any feature selection/extraction step, the accuracy of the models improves with data fusion and reaches 98.8%, 93.5%, and 73.6% for the DCNN, CNN-LSTM, and LSTM methods, respectively, 98.8% being a performance that, according to our reading, has never been reached in the analysis of the faults of a BLDC motor without first going through the extraction of the characteristics and their fusion by traditional methods. These results show that it is possible to work with raw data from multiple sensors and achieve good results using deep learning methods without spending time and resources on selecting appropriate features to extract and methods to use for feature extraction and data fusion.

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Section: Results Of Models Trained On Fused Sensor Datamentioning

confidence: 99%

“…Many industries still use time-based maintenance, which is maintenance performed at fixed time intervals according to a schedule. It is costly because it could lead to unnecessary maintenance when the machines do not encounter any problem [1].…”

Section: Introductionmentioning

confidence: 99%

Modeling and Fault Detection of Brushless Direct Current Motor by Deep Learning Sensor Data Fusion

Fogou

Meisel

Jongmanns

et al. 2022

Sensors

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“…The findings of this study have revealed that for a project manager to oversee major maintenance projects in the energy sector successfully, that project manager must possess the technical experience and engineering skills in this sector. The objectives of any maintenance strategy include a complete balance between capacity, efficiency, availability, reliability, and maintenance costs (Zhao et al, 2022), appropriate to the industrial and economic environment in which the particular plant operates. This further implies that the project manager must also possess some local site-based knowledge, especially regarding the maintenance of power plants.…”

Section: Discussionmentioning

confidence: 99%

Examining critical project management skills for successful delivery of major maintenance projects: insights from the United Kingdom energy sector

Pieterse

Agyekum²,

Manu³

et al. 2022

ECAM

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PurposeMajor maintenance projects are often regarded as maintenance activities regardless of the projects' complexity and scale. Consequently, very scarce research attention has hitherto been paid to the critical skills required when undertaking these projects. More specifically, the body of relevant knowledge is deprived of a study focusing on maintenance projects within the energy sector. In view of this shortcoming, this research aims to examine the critical project management (PM) skills required to deliver major maintenance projects within the energy sector.Design/methodology/approachBased on a quantitative research strategy, this study addressed the knowledge gap through a cross-sectional survey of professionals involved in the delivery of major maintenance projects in the United Kingdom's (UK) energy sector. Data obtained were analyzed via descriptive (e.g. frequencies, mean and standard deviation [SD]) and inferential statistical analyses (One sample t-test and exploratory factor analysis (EFA)).FindingsOut of the 45 PM skills identified in the literature and examined by the respondents, the results obtained from the One sample t-test (based on p (1-tailed) = 0.05) showed that 37 were considered to be at least “important,” accounting for 80.4% of all the skills identified. EFA revealed a clustering of the PM skills items into seven components: “skills related to work scheduling and coordination”; “communication, risk, safety and stakeholder management skills”; “quality assurance skills”; “people management skills”; “skills related to forecasting scope and duration of outage”; “implementation of processes and time management skills” and “technical/engineering skills and experience pertaining to the outage and local site knowledge.”Originality/valueThis study has identified and contributed to the limited state-of-the-art skills project managers must possess to manage major maintenance projects in the energy sector successfully. The findings would be useful to organizations within the energy sector in ensuring that the organizations have suitable personnel in place to deliver major maintenance projects on the organizations' assets.

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“…Within API manufacturing, the increased dependence on equipment has prompted the requirement to keep equipment in good condition [ 45 ] . Maintaining equipment in good condition requires a robust maintenance program with multiple manual and automated tasks [ 46 ].…”

Section: Literature Reviewmentioning

confidence: 99%

A total productive maintenance & reliability framework for an active pharmaceutical ingredient plant utilising design for Lean Six Sigma

Shannon,

Trubetskaya,

Iqbal

et al. 2023

Heliyon

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A Review of Sustainable Maintenance Strategies for Single Component and Multicomponent Equipment

Cited by 26 publications

References 165 publications

Modeling and Fault Detection of Brushless Direct Current Motor by Deep Learning Sensor Data Fusion

Modeling and Fault Detection of Brushless Direct Current Motor by Deep Learning Sensor Data Fusion

Examining critical project management skills for successful delivery of major maintenance projects: insights from the United Kingdom energy sector

A total productive maintenance & reliability framework for an active pharmaceutical ingredient plant utilising design for Lean Six Sigma

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