Framework of machine criticality assessment with criteria interactions

Abstract

Criticality is considered as a fundamental category of production planning, maintenance process planning and management. The criticality assessment of machines and devices can be a structured set of activities allowing to identify failures which have the greatest potential impact on the company’s business goals. It can be also used to define maintenance strategies, investment strategies and development plans, assisting the company in prioritizing their allocations of financial resources to those machines and devices that are critical in accordance with the predefined business criteria. In a criticality assessment process many different and interacting criteria have to be taken into consideration, despite the fact that there is a high level of uncertainty related to various parameters. In addition, not all assessment criteria are equally important. Therefore, it is necessary to determine the weight of each criterion taking into account different requirements of machine criticality process stakeholders. That is why a novel model of a machine criticality assessment is proposed in this paper. The model extends the existing methods of assessing machines criticality, taking into account not only the importance of machine criticality assessment criteria, but also possible interactions between them.

Application of MICMAC, Fuzzy AHP, and Fuzzy TOPSIS for Evaluation of the Maintenance Factors Affecting Sustainable Manufacturing

“…By definition, maintenance is the "combination of all technical, administrative and managerial actions during the life cycle of an item intended to retain it, or restore it to a state in which it can perform the required function" [18]. From this definition, it follows that maintenance is a complex set of activities that cover the entire structure and organization of a manufacturing company [19].…”

Degradation assessment of bearing based on machine learning classification matrix

“…artificial neural network, support vector machine, fuzzy sets theory; fuzzy set theory based expert system used in the diagnosis of the bearing health condition. Artificial neural network on vibration signal data was used for bearing fault diagnosis [20].They trained the model to classify seven different bearing classes and classifier produce the high accuracy diagnosis of real bearing defects. Artificial neural network was discussed for fault diagnosis of rolling element bearing [21].…”

Diagnosing Faults in the Timing System of a Passenger Car Spark Ignition Engine Using the Bayes Classifier and Entropy of Vibration Signals

“…The diagnostic process, aimed at identifying the technical condition of the object, is carried out usually during the normal operation of the object, and it can be done at any stage of its life. Occurrence of damage to the machines or deterioration of their operational status is recognized based on the symptoms, which are represented by the features of diagnostic signals [17,21,29,31,38,39,40,[42][43][44]45].…”