Predictive maintenance techniques

Lü, Bin; Durocher, David B.; Stemper, P.

doi:10.1109/mias.2009.934444

Cited by 60 publications

(17 citation statements)

References 12 publications

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“…The essential data sources that must be found while thinking of a suitable problem for a PdM (Lu et al, 2009) solution are: The training data should contain of examples about normal operating scheme of the automobile part, along with the failure scheme. Maintenance or repair history containing details about replaced components needs to be known and also the condition of the automobile part which will provide a pattern of the performance of the part with varying time.…”

Section: Related Workmentioning

confidence: 99%

Automobile Predictive Maintenance Using Deep Learning

Dash

Raj

Agarwal

et al. 2021

International Journal of Artificial Intelligence and Machine Learning

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There are three types of maintenance management policy Run-tofailure (R2F), Preventive Maintenance (PvM) and Predictive Maintenance (PdM). In both R2F and PdM we have the data related to the maintenance cycle. In case of Preventive Maintenance (PvM) complete information about maintenance cycle is not available. Among these three maintenance policies, predictive Maintenance (PdM) is becoming a very important strategy as it can help us to minimize the repair time and the associated cost with it. In this paper we have proposed PdM, which allows the dynamic decision rules for the maintenance management. PdM is achieved by training the machine learning model with the datasets. It also helps in planning of maintenance schedules. We specially focused on two models that are Binary Classification and Recurrent Neural Network. In Binary Classification we classify whether our data belongs to the failure class or the non failure class. In Binary Classification the number of cycles is entered and classification model predicts whether it belongs to the failure/non failure class.

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Section: Related Workmentioning

confidence: 99%

Automobile Predictive Maintenance Using Deep Learning

Dash

Raj

Agarwal

et al. 2021

International Journal of Artificial Intelligence and Machine Learning

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“…Furthermore, the involvement of upper-management in the process is advised, to foster and select the best repair teams, optimising also the human involvement. Lu et al [8] present a proactive approach to PdM (still being actively used currently) were a prototype sensor system was used to monitor electrical motors and, using noisecancellation models, they were able to predict bearing failure with relative accuracy (they were able to detect that a high risk of failure preemptively in a case scenario). This work showed that prediction may reduce greatly the maintenance costs by reducing production stoppage times.…”

Section: Review Of Intelligent Technology Applied To Maintenancementioning

confidence: 99%

Requirements for an Intelligent Maintenance System for Industry 4.0

García

Costa

Palanca

et al. 2019

Service Oriented, Holonic and Multi-Agent Manufacturing Systems for Industry of the Future

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Recent advances in the development of technological devices and software for Industry 4.0 have pushed a change in the maintenance management systems and processes. Nowadays, in order to maintain a company competitive, a computerised management system is required to help in its maintenance tasks. This paper presents an analysis of the complexities and requirements for maintenance of Industry 4.0. It focuses on intelligent systems that can help to improve the intelligent management of maintenance. Finally, it presents a summary of lessons learned specified as guidelines for the design of such intelligent systems that can be applied horizontally to any company in the Industry.

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“…This feature, in conjunction with other condition monitoring and predictive diagnostics techniques, such as the pump cavitation or loss of vacuum detection, allows plant managers to schedule motors maintenance in a timely fashion, and hence avoids expensive financial cost, especially associated with the loss of production in a process downtime situation due to motor failures in critical applications [5], [9]. Fig.…”

Section: Im Plem Entation In Protective Relaysmentioning

confidence: 99%

“…Such environmental and mechanical stresses could lead to motor degradation and malfunction over the long run. The monitoring and protection of such medium-voltage induction motors is an essential element in the overall industrial processes protection scheme to avoid financial losses caused by unexpected process downtime [5].…”

Section: Introductionmentioning

confidence: 99%

Practical aspects of rotor cage fault detection for medium-voltage induction motors

Lü¹,

Gao

Benzing

2013

2013 IEEE Industry Applications Society Annual Meeting

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Predictive diagnostics offering early failure detection of large induction motors applied in metals, pulp & paper and other process industries are becoming increasingly important. As motors grow larger, industry has become increasingly reliant on technologies to detect rotor faults via on line prognostics and arrange optimal maintenance intervals to increase productivity. Traditional broken rotor bar fault detection algorithms have historically relied largely on monitoring changes in the stator current spectra. This often results in nuisance warnings when the motor operates at different load levels, or when baseline data at healthy motor operations are not available. To address this issue, a fault severity evaluation technique is introduced in this paper to detect rotor cage failures using only current and voltage measurements, plus selected motor nameplate data and motor's geometric dimensions. The fault severity index can indicate the possibility of a rotor cage fault even in the absence of baseline data. This guarantees the algorithm's reliability in practical applications. In addition, a decision-making system, including an adaptive filter and fuzzy logic, is proposed to warn the user in the case of a rotor cage failure. Experimental results show that the proposed fault severity evaluation algorithm can reliably reflect the rotor cage status under different operating conditions, which can be further applied in the detection of rotor cage failures. Index Terms--Induction motor, mechanical stress, medium voltage motor, metal industries, process industries, rotor cage fault, thermal stress.I.

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Predictive maintenance techniques

Cited by 60 publications

References 12 publications

Automobile Predictive Maintenance Using Deep Learning

Automobile Predictive Maintenance Using Deep Learning

Requirements for an Intelligent Maintenance System for Industry 4.0

Practical aspects of rotor cage fault detection for medium-voltage induction motors

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