Remaining useful life prediction and predictive maintenance strategies for multi-state manufacturing systems considering functional dependence

Han, Xiao; Wang, Zili; Xie, Min; He, Yihai; Li, Yao; Wang, Wenzhuo

doi:10.1016/j.ress.2021.107560

Cited by 121 publications

(36 citation statements)

References 34 publications

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“…Kang et al [23] implemented an ML-based approach for automating the prediction of RUL of equipment in continuous production lines. Han et al [24] developed a method for RUL prediction for manufacturing systems using a mission reliability-oriented approach based on the functional dependence of components.…”

Section: Literature Review and Background Studymentioning

confidence: 99%

Maintenance Initiation Prediction Incorporating Vibrations and System Availability

Gopikuttan

Veettil²,

Govindan³

2022

Adv. technol. innov.

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As per ISO-10816, electric motors up to 15 kW are classified as Class I machines, and the major reason for their failure is that the vibrations in them are above the alert limit. This study presents a new model for predicting the condition-based maintenance (CBM) initiation points through vibration measurement in a system of Class I machines. The proposed model follows the accelerated life testing (ALT) procedure. ALT includes the formation of an artificial wear environment in bearings to analyze the resultant system vibrations on system availability. The artificial wear environment created is close to the real industrial situation. The results show that the prediction of the CBM initiation points is based on the established relation between the system availability and vibrations. Furthermore, a relation between the available time for maintenance initiation and different vibration velocities is demonstrated.

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Section: Literature Review and Background Studymentioning

confidence: 99%

Maintenance Initiation Prediction Incorporating Vibrations and System Availability

Gopikuttan

Veettil²,

Govindan³

2022

Adv. technol. innov.

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“…Disregarding deformations, these studies fall within the category of rigid body assembly. Besides, the deviations generated in the service stage are not used to guide the product maintenance or improve the tolerance allocation 18 .…”

Section: Introductionmentioning

confidence: 99%

Privacy-preserving for assembly deviation prediction in a machine learning model of hydraulic equipment under value chain collaboration

Qiu

Feng

et al. 2022

Sci Rep

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Hydraulic equipment, as a typical mechanical product, has been wildly used in various fields. Accurate acquisition and secure transmission of assembly deviation data are the most critical issues for hydraulic equipment manufacturer in the PLM-oriented value chain collaboration. Existing deviation prediction methods are mainly used for assembly quality control, which concentrate in the product design and assembly stage. However, the actual assembly deviations generated in the service stage can be used to guide the equipment maintenance and tolerance design. In this paper, a high-fidelity prediction and privacy-preserving method is proposed based on the observable assembly deviations. A hierarchical graph attention network (HGAT) is established to predict the assembly feature deviations. The hierarchical generalized representation and differential privacy reconstruction techniques are also introduced to generate the graph attention network model for assembly deviation privacy-preserving. A derivation gradient matrix is established to calculate the defined modified necessary index of assembly parts. Two privacy-preserving strategies are designed to protect the assembly privacy of node representation and adjacent relationship. The effectiveness and superiority of the proposed method are demonstrated by a case study with a four-column hydraulic press.

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“…Diagnostic systems play a key role in modern machines and devices. Information about the technical state of a given device has vital importance in many systems' functioning [1,2] and is a subject of meticulous research conducted by numerous academic institutions [3][4][5][6]. There are many diagnostic systems based on vibration measurement using a laser sensor [7,8], classical piezoelectric accelerometers [9,10], shape-memory sensors [11,12], and fiber optic solutions [13,14].…”

Section: Introductionmentioning

confidence: 99%

Predictive Maintenance Neural Control Algorithm for Defect Detection of the Power Plants Rotating Machines Using Augmented Reality Goggles

Lalik

Wątorek

2021

Energies

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The concept of predictive and preventive maintenance and constant monitoring of the technical condition of industrial machinery is currently being greatly improved by the development of artificial intelligence and deep learning algorithms in particular. The advancement of such methods can vastly improve the overall effectiveness and efficiency of systems designed for wear analysis and detection of vibrations that can indicate changes in the physical structure of the industrial components such as bearings, motor shafts, and housing, as well as other parts involved in rotary movement. Recently this concept was also adapted to the field of renewable energy and the automotive industry. The core of the presented prototype is an innovative interface interconnected with augmented reality (AR). The proposed integration of AR goggles allowed for constructing a platform that could acquire data used in rotary components technical evaluation and that could enable direct interaction with the user. The presented platform allows for the utilization of artificial intelligence to analyze vibrations generated by the rotary drive system to determine the technical condition of a wind turbine model monitored by an image processing system that measures frequencies generated by the machine.

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Remaining useful life prediction and predictive maintenance strategies for multi-state manufacturing systems considering functional dependence

Cited by 121 publications

References 34 publications

Maintenance Initiation Prediction Incorporating Vibrations and System Availability

Maintenance Initiation Prediction Incorporating Vibrations and System Availability

Privacy-preserving for assembly deviation prediction in a machine learning model of hydraulic equipment under value chain collaboration

Predictive Maintenance Neural Control Algorithm for Defect Detection of the Power Plants Rotating Machines Using Augmented Reality Goggles

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