A Method for Implementation of Machine Learning Solutions for Predictive Maintenance in Small and Medium Sized Enterprises

Welte, Rebecca; Estler, Manfred; Lücke, Dominik

doi:10.1016/j.procir.2020.04.052

Cited by 33 publications

(13 citation statements)

References 7 publications

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“…, [26], [27], [40], [43], [41], [33], [28], [34], [44], [29], [35], [45], [46], [48], [50], [51], [52], [36], [53], [54], [57], [58], 23…”

Section: Inclusion-exclusion Criteriaunclassified

“…Machine learning, neural networks and deep learning are the mostly commonly used and trending technology in predictive maintenance. These technologies need a systematic and pragmatic methods to incorporate it in SME use cased of predictive maintenance [27]. One of the main characteristics of predictive modeling is that it does not depend on the standard programming practices like object-oriented design principles, but the algorithms learn from data [54] during the training phase.…”

Section: Distinctive Characteristics (Rq3)mentioning

confidence: 99%

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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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“…, [26], [27], [40], [43], [41], [33], [28], [34], [44], [29], [35], [45], [46], [48], [50], [51], [52], [36], [53], [54], [57], [58], 23…”

Section: Inclusion-exclusion Criteriaunclassified

Section: Distinctive Characteristics (Rq3)mentioning

confidence: 99%

A Systematic Mapping Study of Predictive Maintenance in SMEs

et al. 2022

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“…The traditional computational mechanics techniques often cannot handle the complexity of those datasets, for this reason, Machine Learning (ML) has emerged as a solution to this problem. As a result, ML is currently the fastest growing research field [34] and is being successfully used in the field of Predictive Maintenance in multiple major engineering industries [35].…”

Section: Motivationmentioning

confidence: 99%

Predictive Maintenance of an External Gear Pump using Machine Learning Algorithms

Lakshmanan¹

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The importance of Predictive Maintenance is critical for engineering industries, such as manufacturing, aerospace and energy. Unexpected failures cause unpredictable downtime, which can be disruptive and high costs due to reduced productivity. This forces industries to ensure the reliability of their equip-ment. In order to increase the reliability of equipment, maintenance actions, such as repairs, replacements, equipment updates, and corrective actions are employed. These actions aﬀect the ﬂexibility, quality of operation and manu-facturing time. It is therefore essential to plan maintenance before failure occurs.Traditional maintenance techniques rely on checks conducted routinely based on running hours of the machine. The drawback of this approach is that maintenance is sometimes performed before it is required. Therefore, conducting maintenance based on the actual condition of the equipment is the optimal solu-tion. This requires collecting real-time data on the condition of the equipment, using sensors (to detect events and send information to computer processor).Predictive Maintenance uses these types of techniques or analytics to inform about the current, and future state of the equipment. In the last decade, with the introduction of the Internet of Things (IoT), Machine Learning (ML), cloud computing and Big Data Analytics, manufacturing industry has moved forward towards implementing Predictive Maintenance, resulting in increased uptime and quality control, optimisation of maintenance routes, improved worker safety and greater productivity.The present thesis describes a novel computational strategy of Predictive Maintenance (fault diagnosis and fault prognosis) with ML and Deep Learning applications for an FG304 series external gear pump, also known as a domino pump. In the absence of a comprehensive set of experimental data, synthetic data generation techniques are implemented for Predictive Maintenance by perturbing the frequency content of time series generated using High-Fidelity computational techniques. In addition, various types of feature extraction methods considered to extract most discriminatory informations from the data. For fault diagnosis, three types of ML classiﬁcation algorithms are employed, namely Multilayer Perceptron (MLP), Support Vector Machine (SVM) and Naive Bayes (NB) algorithms. For prognosis, ML regression algorithms, such as MLP and SVM, are utilised. Although signiﬁcant work has been reported by previous authors, it remains diﬃcult to optimise the choice of hyper-parameters (important parameters whose value is used to control the learning process) for each speciﬁc ML algorithm. For instance, the type of SVM kernel function or the selection of the MLP activation function and the optimum number of hidden layers (and neurons).It is widely understood that the reliability of ML algorithms is strongly depen-dent upon the existence of a suﬃciently large quantity of high-quality training data. In the present thesis, due to the unavailability of experimental data, a novel high-ﬁdelity in-silico dataset is generated via a Computational Fluid Dynamic (CFD) model, which has been used for the training of the underlying ML metamodel. In addition, a large number of scenarios are recreated, ranging from healthy to faulty ones (e.g. clogging, radial gap variations, axial gap variations, viscosity variations, speed variations). Furthermore, the high-ﬁdelity dataset is re-enacted by using degradation functions to predict the remaining useful life (fault prognosis) of an external gear pump.The thesis explores and compares the performance of MLP, SVM and NB algo-rithms for fault diagnosis and MLP and SVM for fault prognosis. In order to enable fast training and reliable testing of the MLP algorithm, some predeﬁned network architectures, like 2n neurons per hidden layer, are used to speed up the identiﬁcation of the precise number of neurons (shown to be useful when the sample data set is suﬃciently large). Finally, a series of benchmark tests are presented, enabling to conclude that for fault diagnosis, the use of wavelet features and a MLP algorithm can provide the best accuracy, and the MLP al-gorithm provides the best prediction results for fault prognosis. In addition, benchmark examples are simulated to demonstrate the mesh convergence for the CFD model whereas, quantiﬁcation analysis and noise inﬂuence on training data are performed for ML algorithms.

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“…predictive maintenance and quality control. Nevertheless, once again, the lack of real-world integration in SMEs is mainly due to the lack of knowledge and resources [141]. To overcome this, pilot projects are suitable starting methods for SMEs.…”

Section: Use Cases Of Aimentioning

confidence: 99%

“…Which further indicates the challenges and knowledge required for implementation. Therefore, the problem is more present in SMEs where, in general, the expertise and resources is a constraint (Welte et al, 2020). Henceforth, pilot projects are a way for SMEs to get started with AI, either in collaboration with a research institution or an AI consultancy company.…”

Section: The Use Of Ai In Smesmentioning

confidence: 99%

AI and IoT for Production Data Analytics in SMEs

Hansen¹

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A Method for Implementation of Machine Learning Solutions for Predictive Maintenance in Small and Medium Sized Enterprises

Cited by 33 publications

References 7 publications

A Systematic Mapping Study of Predictive Maintenance in SMEs

A Systematic Mapping Study of Predictive Maintenance in SMEs

Predictive Maintenance of an External Gear Pump using Machine Learning Algorithms

AI and IoT for Production Data Analytics in SMEs

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