Machine learning and AI for long-term fault prognosis in complex manufacturing systems

Bukkapatnam, Satish T. S.; Afrin, Kahkashan; Dave, Darpit; Kumara, Soundar

doi:10.1016/j.cirp.2019.04.104

Cited by 33 publications

(12 citation statements)

References 12 publications

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“…As can be seen in Fig. 6, 53.3% of publications employ supervised learning techniques, 28.9% use unsupervised learning techniques, 15.6% make use of both supervised and unsupervised techniques and 2.2% [56] conference Advances in Manufacturing [57] journal Applied Sciences [58] journal Business & Information Systems Engineering [59] journal Complexity [60] journal Computers & Industrial Engineering [61] journal Electronics [62] journal Engineering Applications of Artificial Intelligence [63] journal Expert Systems with Applications [64] journal IEEE Transactions on Industrial Electronics [31] journal IEEE Transactions on Industrial Informatics [65] journal Journal of Manufacturing Systems [66] journal Simulation Modelling Practice and Theory [67] journal Studies in Informatics and Control [68] journal 2019 31st International Conference on Advanced Information Systems Engineering (CAiSE) [69,70] conference CIRP Annals [71,72] journal Sensors [73,74] journal The International Journal of Advanced Manufacturing Technology [75,76] journal IEEE Access [77][78][79][80][81] journal Fig. 4 Proportion of publications in conferences and journals combine semi-supervised, unsupervised, and supervised techniques.…”

Section: Rq3: What Machine Learning Algorithms and Methods Are Curren...mentioning

confidence: 99%

“…The use of unsupervised techniques is motivated mostly by an absence of labeled data [47, 48, 50, 54, 59-62, 65, 70], although in some studies they are employed to detect outliers [74], reduce dimensionality [31,75] or extract features [81]. In studies [39,42,45,58,66,69,71], labeled data was available, but was used to validate the unsupervised learning models.…”

Section: Rq3: What Machine Learning Algorithms and Methods Are Curren...mentioning

confidence: 99%

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Machine learning techniques applied to mechanical fault diagnosis and fault prognosis in the context of real industrial manufacturing use-cases: a systematic literature review

2022

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When put into practice in the real world, predictive maintenance presents a set of challenges for fault detection and prognosis that are often overlooked in studies validated with data from controlled experiments, or numeric simulations. For this reason, this study aims to review the recent advancements in mechanical fault diagnosis and fault prognosis in the manufacturing industry using machine learning methods. For this systematic review, we searched Web of Science, ACM Digital Library, Science Direct, Wiley Online Library, and IEEE Xplore between January 2015 and October 2021. Full-length studies that employed machine learning algorithms to perform mechanical fault detection or fault prognosis in manufacturing equipment and presented empirical results obtained from industrial case-studies were included, except for studies not written in English or published in sources other than peer-reviewed journals with JCR Impact Factor, conference proceedings and book chapters/sections. Of 4549 records, 44 primary studies were selected. In 37 of those studies, fault diagnosis and prognosis were performed using artificial neural networks (n=12), decision tree methods (n=11), hybrid models (n=8), or latent variable models (n=6), with one of the studies employing two different types of techniques independently. The remaining studies employed a variety of machine learning techniques, ranging from rule-based models to partition-based algorithms, and only two studies approached the problem using online learning methods. The main advantages of these algorithms include high performance, the ability to uncover complex nonlinear relationships and computational efficiency, while the most important limitation is the reduction in model performance in the presence of concept drift. This review shows that, although the number of studies performed in the manufacturing industry has been increasing in recent years, additional research is necessary to address the challenges presented by real-world scenarios.

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Section: Rq3: What Machine Learning Algorithms and Methods Are Curren...mentioning

confidence: 99%

Section: Rq3: What Machine Learning Algorithms and Methods Are Curren...mentioning

confidence: 99%

Machine learning techniques applied to mechanical fault diagnosis and fault prognosis in the context of real industrial manufacturing use-cases: a systematic literature review

2022

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“…The manufacturing industry is adopting IoT devices at 40% annual growth rates for enhanced asset management and increased productivity [109]. The proliferation of IoT and other noncompute devices is increasing the diversity of devices connected to the network in the next-generation manufacturing system [110].…”

Section: E Securing Manufacturing Iot Network By Device Population Diversitymentioning

confidence: 99%

A Survey of Cybersecurity of Digital Manufacturing

et al. 2021

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The Industry 4.0 concept promotes a digital manufacturing (DM) paradigm that can enhance quality and productivity, which reduces inventory and the lead time for delivering custom, batch-of-one products based on achieving convergence of additive, subtractive, and hybrid manufacturing machines, automation and robotic systems, sensors, computing, and communication networks, artificial intelligence, and big data. A DM system consists of embedded electronics,

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“…On the other hand, if we use 3D printing, it would allow us to build the entire car body of the product, make the necessary adjustments, change the design, in an economical and fast way; if 3D printing goes hand in hand with artificial intelligence [4], we would obtain additional potential, having the ability to see what the market demands in real time, changes in production volume, changes in design, order and cancellations, resulting in a decrease in costs, which will give the company a competitive advantage.…”

Section: Artificial Intelligence and Disruptive Technologies That Support Productionmentioning

confidence: 99%

Artificial Intelligence as a Competitive Advantage in the Manufacturing Area

Valencia

Moreno

Sanchez

2019

Communications in Computer and Information Science

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Since the beginning of the industrial revolution, manufacturing has gone through different stages: the 1st technological islands, 2nd the mass production, 3rd the lean manufacturing and the 4th IIoT (for the year 2025); we must keep in mind the leadership in the production of goods today what the Eastern countries have (and are using stage 3), so that the current guidelines have the necessary meaning, which establishes a new way of producing more as the potential of technologies that are in the process of maturation such as: Artificial Intelligence, Big Data, 3D Printing and Robotics; find original solutions to the problems of productivity, customization, just in time and services.Artificial Intelligence is taking a leading role in solving manufacturing problems, with the purpose of eliminating all those areas that are blindly worked, and that therefore it is not possible to improve by suffering from data to: analyze them, obtain information, establish controls and improve. The above is achieved by establishing disruptive technologies that take control in real time.

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Machine learning and AI for long-term fault prognosis in complex manufacturing systems

Cited by 33 publications

References 12 publications

Machine learning techniques applied to mechanical fault diagnosis and fault prognosis in the context of real industrial manufacturing use-cases: a systematic literature review

Machine learning techniques applied to mechanical fault diagnosis and fault prognosis in the context of real industrial manufacturing use-cases: a systematic literature review

A Survey of Cybersecurity of Digital Manufacturing

Artificial Intelligence as a Competitive Advantage in the Manufacturing Area

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