Condition-Based Maintenance of HVAC on a High-Speed Train for Fault Detection

Abstract

Reliability-centered maintenance (RCM) is a well-established method for preventive maintenance planning. This paper focuses on the optimization of a maintenance plan for an HVAC (heating, ventilation and air conditioning) system located on high-speed trains. The first steps of the RCM procedure help in identifying the most critical items of the system in terms of safety and availability by means of a failure modes and effects analysis. Then, RMC proposes the optimal maintenance tasks for each item making up the system. However, the decision-making diagram that leads to the maintenance choice is extremely generic, with a consequent high subjectivity in the task selection. This paper proposes a new fuzzy-based decision-making diagram to minimize the subjectivity of the task choice and preserve the cost-efficiency of the procedure. It uses a case from the railway industry to illustrate the suggested approach, but the procedure could be easily applied to different industrial and technological fields. The results of the proposed fuzzy approach highlight the importance of an accurate diagnostics (with an overall 86% of the task as diagnostic-based maintenance) and condition monitoring strategy (covering 54% of the tasks) to optimize the maintenance plan and to minimize the system availability. The findings show that the framework strongly mitigates the issues related to the classical RCM procedure, notably the high subjectivity of experts. It lays the groundwork for a general fuzzy-based reliability-centered maintenance method.

A Review of Data-Driven Approaches and Techniques for Fault Detection and Diagnosis in HVAC Systems

“…On the other hand, the literature suggests that the knowledge discovery approach can also be combined with data-driven approaches, especially pattern recognition techniques [ 36 , 96 , 97 , 98 ]. Such solutions employ techniques such as fuzzy logic [ 99 ], rule data mining [ 95 , 96 , 97 ], and clustering [ 95 ]. Bayes-based techniques are commonly used as well, e.g., DBN [ 86 , 94 ], Bayesian Inference [ 85 ], and Bayesian Method [ 89 ].…”

Reliability Improvement of Railway Vehicles: A Review

“…Researches that apply reliability analysis in reliability improvement include [3], [11], [12], [13], [14], [15], [17], [19]- [22] [23]- [32], [34], [36]. Meanwhile, another researches that practice maintenance strategies for reliability improvement include [10], [16], [18], [35], [37], [38]. In addition, the only research that performs design optimization in reliability improvement is [33].…”

“…No Authors, Year Title Summary 2. [11] Reliability Analysis on the Bogie System at Indonesian High-Speed Trains in The Design Phase to Improve Service Quality [12] Reliability Analysis of Metro Door System Based on FMECA [13] Evaluation of the Adequacy of Maintenance Tasks Using the Failure Consequences of Railroad Vehicles [14] Optimizing Inspection Intervals-Reliability and Availability in Terms of a Cost Model: a Case Study on Railway Carriers [15] Condition-Based Maintenance of HVAC on a High-Speed Train for Fault Detection [16] Advanced Maintenance Cycle Optimization of Urban Rail Transit Vehicles. [17] The paper explains that reliability analysis is required to identify the bottlenecks in the system and find the components or subsystems with low reliability for a desired level of performance.…”

Logic Solver Diagnostics in Safety Instrumented Systems for Oil and Gas Applications

“…This procedure is the core of predictive maintenance policies. As a matter of fact, the use of CM provides all the necessary information to schedule maintenance activities promptly and prevent unexpected failures [8][9][10][11][12]. Thus, cost reduction is guaranteed, maximizing the system uptime and optimizing the production efficiency.…”