Development of a speed invariant deep learning model with application to condition monitoring of rotating machinery

Lee, Wo Jae; Xia, Kevin; Denton, Nancy L.; Ribeiro, Bruno; Sutherland, John W.

doi:10.1007/s10845-020-01578-x

Cited by 35 publications

(7 citation statements)

References 19 publications

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“…(2) Deep learning classification networks For the purpose of effective classification of more complex features, some deep network models are also used in combination with signal analysis techniques to provide fault diagnosis methods based on independent feature extraction. Lee et al [91] developed a CWT-CNN model to detect the mechanical fault in variable speed settings. They collected machine state data at different rotational speeds using a triaxial accelerometer, extracted time-frequency features through continuous wavelet transform with Morlet wavelets, and applied them to a convolutional neural network (CNN) model.…”

Section: Methods Based On Independent Feature Extractionmentioning

confidence: 99%

A Review of Real-Time Fault Diagnosis Methods for Industrial Smart Manufacturing

Yan

Jing

et al. 2023

Processes

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In the era of Industry 4.0, highly complex production equipment is becoming increasingly integrated and intelligent, posing new challenges for data-driven process monitoring and fault diagnosis. Technologies such as IIoT, CPS, and AI are seeing increasing use in modern industrial smart manufacturing. Cloud computing and big data storage greatly facilitate the processing and management of industrial information flow, which helps the development of real-time fault diagnosis (RTFD) technology. This paper provides a comprehensive review of the latest RTFD technologies in the field of industrial process monitoring and machine condition monitoring. The RTFD process is introduced in detail, starting with the data acquisition process. The current RTFD methods are divided into methods based on independent feature extraction, methods based on “end-to-end” neural networks, and methods based on qualitative knowledge reasoning from a new perspective. In addition, this paper discusses the challenges and potential trends of RTFD in future development to provide a reference for researchers focusing on this field.

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Section: Methods Based On Independent Feature Extractionmentioning

confidence: 99%

A Review of Real-Time Fault Diagnosis Methods for Industrial Smart Manufacturing

Yan

Jing

et al. 2023

Processes

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“…Extending the lifetime of manufacturing equipment and products is a significant strategy to reduce environmental impact in many circumstances [outlined in Figure 2]. Lengthening a production equipment life cycle is often associated with the concept of prognostics and health management (PHM) or predictive maintenance in smart manufacturing [38] . PHM aims to provide users with an integrated view of the health state of a single piece of equipment or an overall system, in which diagnostic and prognostic analyses are conducted to detect an incipient failure and predict the remaining useful life (RUL).…”

Section: Smart Manufacturingmentioning

confidence: 99%

Perspectives on future research directions in green manufacturing for discrete products

Triebe¹,

Deng²,

Pérez-Cardona³

et al. 2023

Green Manuf Open

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With the increasing concern due to climate change caused by a higher atmospheric concentration of CO2 and other greenhouse gases, reducing environmental impact is becoming more important for every part of society. Manufacturing is responsible for a significant amount of energy/material consumption and environmental burden and, therefore, has a great opportunity to reduce its impact through green manufacturing. Green manufacturing presents opportunities across the manufacturing enterprise to increase the efficient usage of energy and material resources. These opportunities include designing products to consume fewer materials and energy during manufacturing and use, incorporating more efficient manufacturing processes, streamlining and optimizing manufacturing schedules and plans, and circularizing products. The goal of this paper will be to provide a perspective from the authors on the opportunities that exist within green manufacturing for discrete products through a review of pertinent topics and future directions. The paper will focus on processes, manufacturing equipment, manufacturing systems, recovering value at a product’s end-of-life, and additional thoughts that include metrics and indicators, techno-economic assessment, and a discussion of efficiency and effectiveness. Key findings from this review include a need for social indicators and renewable energy considerations in scheduling and process planning, integrating Industry 4.0 into circular economy along with social and institutional dimensions, consistency in the ability to measure and conceptualize metrics and indicators, a detailed evaluation of the life cycle impacts and cost of Addit Manuf, and more human and environment-oriented considerations for smart manufacturing.

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“…For example, Liu et al [138] proposed a low-speed lightweight RNN, which has a small storage space occupancy rate and low calculation delay. Miki et al [139] proposed a LTSM-based method for time-series analysis and a training method for weakly supervised training. Rao et al [140] proposed a many-to-many-to-one bi-directional LSTM to automatically extract the rotating speed from vibration signals.…”

Section: Vanilla Fault Diagnosismentioning

confidence: 99%

Challenges and Opportunities of AI-Enabled Monitoring, Diagnosis & Prognosis: A Review

Zhao

et al. 2021

Chin. J. Mech. Eng.

101

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Prognostics and Health Management (PHM), including monitoring, diagnosis, prognosis, and health management, occupies an increasingly important position in reducing costly breakdowns and avoiding catastrophic accidents in modern industry. With the development of artificial intelligence (AI), especially deep learning (DL) approaches, the application of AI-enabled methods to monitor, diagnose and predict potential equipment malfunctions has gone through tremendous progress with verified success in both academia and industry. However, there is still a gap to cover monitoring, diagnosis, and prognosis based on AI-enabled methods, simultaneously, and the importance of an open source community, including open source datasets and codes, has not been fully emphasized. To fill this gap, this paper provides a systematic overview of the current development, common technologies, open source datasets, codes, and challenges of AI-enabled PHM methods from three aspects of monitoring, diagnosis, and prognosis.

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Development of a speed invariant deep learning model with application to condition monitoring of rotating machinery

Cited by 35 publications

References 19 publications

A Review of Real-Time Fault Diagnosis Methods for Industrial Smart Manufacturing

A Review of Real-Time Fault Diagnosis Methods for Industrial Smart Manufacturing

Perspectives on future research directions in green manufacturing for discrete products

Challenges and Opportunities of AI-Enabled Monitoring, Diagnosis & Prognosis: A Review

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