A generalized three-type lifetime probabilistic models-based hybrid maintenance policy with a practical switcher for time-based preventive maintenance and condition-based maintenance

Author(s):  
Ming-Yi You

This paper proposes a generalized hybrid maintenance policy for maintenance scheduling with the help of both time-based maintenance and condition-based maintenance techniques, in which three-type product lifetime probabilistic models are utilized. A dispersion of lifetime estimates-based switcher is proposed to recommend the choice of time-based maintenance or condition-based predictive maintenance schedules in a real-time manner. Within the condition-based predictive maintenance policy, which is a part of the hybrid maintenance policy, a novel weighted average of the maintenance schedules is proposed to recommend maintenance acts, which are estimated based on two types of product lifetime probabilistic models namely type-II and type-III lifetime probabilistic models. The hybrid maintenance policy includes the classical time-based maintenance policy, the traditional condition-based predictive maintenance policy, and the proposed condition-based predictive maintenance policy as special cases. An extensive numerical investigation for the stochastic linear degradation model verifies the effectiveness of the proposed hybrid maintenance policy, highlighting the existence of a special space among time-based maintenance and condition-based predictive maintenance polices, which provides even better maintenance performance than a solely condition-based predictive maintenance policy.

2017 ◽  
Vol 34 (7) ◽  
pp. 1123-1135 ◽  
Author(s):  
Ming-Yi You

Purpose The purpose of this paper is to propose a predictive maintenance (PdM) system for hybrid degradation processes with continuous degradation and sudden damage to improve maintenance effectiveness. Design/methodology/approach The PdM system updates the degradation model using partial condition monitoring information based on degradation type judgment. In addition, an extended multi-step-ahead updating stopping condition is adopted for performance enhancement of the PdM system. Findings An extensive numerical investigation compares the performance of the PdM system with the corresponding preventive maintenance (PM) policy. By carefully choosing the updating stopping condition, the PdM policy performs better than the corresponding PM policy. Research limitations/implications The proposed PdM system is applicable to single-unit systems. And the continuous degradation process should be well modeled by the stochastic linear degradation model (Gebraeel et al., 2009). Originality/value In literature, there are abundant studies on PdM policies for continuous degradation processes. However, research on hybrid degradation processes still focuses on condition-based maintenance policy and a PdM policy for a hybrid degradation process is still unreported. In this paper, a PdM system for hybrid degradation processes with continuous degradation and sudden damage is proposed. The PdM system decides PM schedules by fully utilizing the condition monitoring data of each specific product, and can hopefully improve maintenance effectiveness.


2017 ◽  
Vol 30 (3) ◽  
pp. 1242-1257 ◽  
Author(s):  
Yiwei WANG ◽  
Christian GOGU ◽  
Nicolas BINAUD ◽  
Christian BES ◽  
Raphael T. HAFTKA ◽  
...  

2019 ◽  
Vol 10 (4) ◽  
pp. 1993-2004
Author(s):  
Parth Pradhan ◽  
Shalinee Kishore ◽  
Boris Defourny

Author(s):  
Giovanni Carabin ◽  
Erich Wehrle ◽  
Renato Vidoni

We are in the era of the fourth industrial revolution. Which highlights adaptability, monitoring, digitisation and efficiency in manufacturing as a result of the design of new smart mechanical systems. A central role in Industry 4.0 is played by maintenance and, within this framework, we define and review condition-based predictive maintenance. Thereafter, we propose a new class of smart mechanical systems that self-optimise utilising both condition-based maintenance and dynamic system modification. Akin to smart structures, smart mechanical systems will recognise and predict faults or malfunctions and, subsequently, self-optimise to restore desirable system behaviour. Potential benefits include increased reliability and efficiency while reducing cost without the requirement of highly skilled technicians. Thus, small and medium-sized enterprises are a specific target of such technology due to their increasing level of automatisation within Industry 4.0.


Author(s):  
C. K. M. Lee ◽  
Yi Cao ◽  
Kam Hung Ng

Maintenance aims to reduce and eliminate the number of failures occurred during production as any breakdown of machine or equipment may lead to disruption for the supply chain. Maintenance policy is set to provide the guidance for selecting the most cost-effective maintenance approach and system to achieve operational safety. For example, predictive maintenance is most recommended for crucial components whose failure will cause severe function loss and safety risk. Recent utilization of big data and related techniques in predictive maintenance greatly improves the transparency for system health condition and boosts the speed and accuracy in the maintenance decision making. In this chapter, a Maintenance Policies Management framework under Big Data Platform is designed and the process of maintenance decision support system is simulated for a sensor-monitored semiconductor manufacturing plant. Artificial Intelligence is applied to classify the likely failure patterns and estimate the machine condition for the faulty component.


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