Prologue: Predictive Maintenance in Dynamic Systems

Lughofer, Edwin; Sayed‐Mouchaweh, Moamar

doi:10.1007/978-3-030-05645-2_1

Cited by 19 publications

(24 citation statements)

References 65 publications

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“…It should be noted that, this overview is limited and serves only to give a general idea of the capabilities of predictive modeling in the field of maintenance. For more information on predictive models, one can refer to [11,[14][15][16][17][18][19]. The decision tree model presents inputs and an output in a flowchart-like structure, which makes it easy to interpret.…”

Section: Alternative Predictive Modelsmentioning

confidence: 99%

Artificial intelligence for predictive maintenance

Kamel

2022

J. Phys.: Conf. Ser.

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Maintenance constitutes an important share of modern industrial activities. Reliable operations rely on the adequate application on maintenance. However, in the present competitive environment, maintenance processes must be optimized so that they will be performed only when needed, otherwise resources will be needlessly wasted. This is in contrast to the conventional approach where maintenance is scheduled according to a time plan regardless of it is needed or not. This paper presents the application of artificial intelligence to create a model that can successfully predict the condition of a machine in terms of the probability of failure occurrence. This work uses a synthetic dataset that reflects a realistic scenario where sensors are connected to a machine to monitor its health condition and record failure incidents. The dataset consists of 10,000 records. Each record consists of five numerical measurements: air and process temperatures, machine rotational speed and torque and finally a measurement of machine wear. This in addition to the type of product the machine is producing makes six input variables. The output response was considered as the state of machine failure that was represented as true or false. An artificial neural network was trained and it successfully predicted the state of the machine. The capabilities and limitations of applying artificial intelligence were discussed. In addition, a brief overview of other predictive techniques was given.

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Section: Alternative Predictive Modelsmentioning

confidence: 99%

Artificial intelligence for predictive maintenance

Kamel

2022

J. Phys.: Conf. Ser.

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“…Operators with augmented reality devices which superimpose digital elements on physical objects are appearing in factories. In the maintenance sector, equipment with sensors that send continuous data on their health status allows the emergence of a new form of maintenance (i.e., predictive maintenance) based on the analysis of past data and predictions (Lughofer & Sayed-Mouchaweh, 2019). In the medical field, hospital staff is confronted with new intelligent assistants (i.e., artificial intelligence) helping them to make diagnoses or propose treatments (Jiang et al, 2017;Powles & Hodson, 2017).…”

Section: Digital Technologiesmentioning

confidence: 99%

How Digital Technologies Modify The Work Characteristics: A Preliminary Study

2021

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New technologies with unprecedented agentic capabilities (i.e., action selection, protocol development) are now introduced in organizations such as Big Data, 3D printing or artificial intelligence. Because they are endowed with novel capabilities that might compete with human agency, they might disrupt the way employees work. Based on the work design model, this study aims to examine their introduction in the daily work activities and the consequent perceptions of the work characteristics. Building on Murray’s et al. (2020) proposal, we offer a classification of the digital technologies to conceptualize their relationship with the work characteristics. To explore the changes induced by two digital technologies (i.e., drones, robotic automation process), we interviewed 3 types of employees (i.e., experts, managers, users) from an organization which has started a digitalization process and we conducted a thematic analysis. Our analysis revealed three main themes that are discussed: A technological theme (arresting, assisting), a work characteristic theme and a theme about the human-technology relationship (agentic, non-agentic). Results showed that employee autonomy has not been reduced when digital technologies executed repetitive and unmotivated tasks and that jobs in the digital work context may be marked by a high level of knowledge characteristics. Moreover, technologies with agentic capabilities may be perceived as a non-human agent. Theoretical contributions for the work design model are then examined.

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“…The data from the embedded electrochemical and optic-based sensors can be used for anomaly detection, defect localization, prognostics, forecasting, diagnosis, optimization, and control in industrial applications. 1 Table II provides examples of sensors and their potential use in the industry.…”

Section: Sensors In the Industrymentioning

confidence: 99%

“…Continuous monitoring of such variables, predicting failures or degradation and taking actions to prevent them is referred to as Predictive Maintenance (PM). 1 PM is one of the most important components of smart manufacturing and Industry 4.0. A recent report from "Allied Market Research" predicted that the market for PM will be worth $23 billion by 2026.…”

mentioning

confidence: 99%

Review—Deep Learning Methods for Sensor Based Predictive Maintenance and Future Perspectives for Electrochemical Sensors

Namuduri

Narayanan

Davuluru

et al. 2020

J. Electrochem. Soc.

116

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The downtime of industrial machines, engines, or heavy equipment can lead to a direct loss of revenue. Accurate prediction of such failures using sensor data can prevent or reduce the downtime. With the availability of Internet of Things (IoT) technologies, it is possible to acquire the sensor data in real-time. Machine Learning and Deep Learning (DL) algorithms can then be used to predict the part and equipment failures, given enough historical data. DL algorithms have shown significant advances in problems where progress has eluded the practitioners and researchers for several decades. This paper reviews the DL algorithms used for predictive maintenance and presents a case study of engine failure prediction. We also discuss the current use of sensors in the industry and future opportunities for electrochemical sensors in predictive maintenance.

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Prologue: Predictive Maintenance in Dynamic Systems

Cited by 19 publications

References 65 publications

Artificial intelligence for predictive maintenance

Artificial intelligence for predictive maintenance

How Digital Technologies Modify The Work Characteristics: A Preliminary Study

Review—Deep Learning Methods for Sensor Based Predictive Maintenance and Future Perspectives for Electrochemical Sensors

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