Expert systems for maintenance in the CIM concept

Majstorovic, Vidosav D.; Milačić, Vladimir R.

doi:10.1016/0166-3615(90)90086-5

Cited by 7 publications

(3 citation statements)

References 2 publications

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“…Since 1980s, ES and ES-related types of IS i.e. knowledge-based systems have been used to solve various problems in manufacturing systems, including various design problems (Basu et al 1995), planning (Singh and Sekhon 2005), machine monitoring, diagnosis and maintenance (Majstorovic and Milacic 1990), system configuration, managing complexity and uncertainty (Monostroni 2003), process control and monitoring (Barschdorff et al 1997;Lekova and Batanov 1998), quality assurance (Sibalija 1992;Smith and Dagli 1994;Westkamper 1997;El-Shal and Morris 2000) etc.…”

Section: Multiresponse Optimisation Using Expert Systemmentioning

confidence: 99%

Review of Multiresponse Process Optimisation Methods

Šibalija

Majstorovic

2015

Advanced Multiresponse Process Optimisation

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The review of methods used for multiresponse process parameter design and similar multiobjective optimisation problems is discussed in this chapter, implying the following classification: (1) conventional methods based on statistical or mathematical techniques: (i) experimental design techniques (response surface methodology, Taguchi's robust parameter design and related approaches), and (ii) iterative mathematical search techniques; (2) non-conventional methods based on artificial intelligence techniques: (i) fuzzy logic, (ii) artificial neural networks, (iii) metaheuristic search techniques (genetic algorithm, simulated annealing, particle swarm optimisation, ant colony optimisation, tabu search, and recent evolutionary algorithms such as artificial bee colony algorithm, biogeography-based optimisation, and teaching-learning-based optimisation), and (iv) expert systems.

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Section: Multiresponse Optimisation Using Expert Systemmentioning

confidence: 99%

Review of Multiresponse Process Optimisation Methods

Šibalija

Majstorovic

2015

Advanced Multiresponse Process Optimisation

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“…It was the state of the art of maintenance in the early 1990′s. Publications such as [9,114,115] describe how knowledge based models were used to perform diagnostics in technical systems. Knowledge based models remain an important field of research for maintenance purposes and three main topics were identified in the systematic literature review: rule-based models, case-based models and fuzzy knowledge-based models.…”

Section: Knowledge-based Modelsmentioning

confidence: 99%

Towards multi-model approaches to predictive maintenance: A systematic literature survey on diagnostics and prognostics

Jimenez

Schwartz

Vingerhoeds

et al. 2020

Journal of Manufacturing Systems

153

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The use of a modern technological system requires a good engineering approach, optimized operations, and proper maintenance in order to keep the system in an optimal state. Predictive maintenance focuses on the organization of maintenance actions according to the actual health state of the system, aiming at giving a precise indication of when a maintenance intervention will be necessary. Predictive maintenance is normally implemented by means of specialized computational systems that incorporate one of several models to fulfil diagnostics and prognostics tasks. As complexity of technological systems increases over time, single-model approaches hardly fulfil all functions and objectives for predictive maintenance systems. It is increasingly common to find research studies that combine different models in multi-model approaches to overcome complexity of predictive maintenance tasks, considering the advantages and disadvantages of each single model and trying to combine the best of them. These multi-model approaches have not been extensively addressed by previous review studies on predictive maintenance. Besides, many of the possible combinations for multi-model approaches remain unexplored in predictive maintenance applications; this offers a vast field of opportunities when architecting new predictive maintenance systems. This systematic survey aims at presenting the current trends in diagnostics and prognostics giving special attention to multi-model approaches and summarizing the current challenges and research opportunities.

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“…The firsts use previous experience to infer rules for detecting faults; the second use physical knowledge or statistical estimation methods to provide a representation of the model describing the phenomenon under observation; the third use historical and real‐time data to understand when a fault is about to occur. For a long time the PdM approaches relied only on knowledge‐base (Majstorović & Milačić, 1990; Vingerhoeds et al, 1995) and model‐based techniques (Marjanović et al, 2011; Richalet, 1993). However, this type of approach has several drawbacks as pointed out by different works such as Nguyen and Medjaher (2019) and Ditzler et al (2015).…”

Section: Introductionmentioning

confidence: 99%

Predictive maintenance for offshore oil wells by means of deep learning features extraction

et al. 2022

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Nowadays, the great diffusion of the Internet of Things and the improvements in Artificial Intelligence techniques have given a rise in the development and application of data‐driven approaches for Predictive Maintenance to reduce the costs linked to the maintenance of industrial machinery. Due to the wide real‐life applications and the strong interest by even more industries, this field is highly attractive for academics and practitioners. So, constructing efficient frameworks to address the Predictive Maintenance problem is an open debate. In this work, we propose a Deep Learning approach for the feature extraction in the offshore oil wells monitoring context, exploiting the public 3 W dataset, which is well‐known in the literature. The dataset is made up of about 2000 multivariate time series labelled according to the corresponding functioning of the well. So, there is a classification task with eight classes, each related to a particular machinery condition. Thanks to the peculiarities of the labels, the proposed framework is valid both for diagnostics and prognostics. In more detail, we compare two different approaches in feature extraction. The first is a statistical approach, widely used in the literature related to the considered dataset; the second is based on Convolutional 1D AutoEncoder. The extracted features are then used as input for several Machine Learning algorithms, namely the Random Forest, Nearest Neighbours, Gaussian Naive Bayes and Quadratic Discriminant Analysis. Different experiments on various time horizons prove the worthiness of the Convolutional AutoEncoder.

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Expert systems for maintenance in the CIM concept

Cited by 7 publications

References 2 publications

Review of Multiresponse Process Optimisation Methods

Review of Multiresponse Process Optimisation Methods

Towards multi-model approaches to predictive maintenance: A systematic literature survey on diagnostics and prognostics

Predictive maintenance for offshore oil wells by means of deep learning features extraction

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