انتخاب تکنیک های تشخیصی و ابزار دقیق در یک برنامه نگهداری پیشگویانه.مطالعه موردی

Predictive maintenance programs (PMPs) can provide significant advantages in relation to quality, safety, availability and cost reduction in industrial plants. Nevertheless, during implementation, different decision making processes are involved, such as the selection of the most suitable diagnostic techniques. A wrong decision can lead to the failure of the setting up of the predictive maintenance program and its elimination, with the consequent economic losses, as the setting up of these programs is a strategic decision. In this article, a model is proposed that carries out the decision making in relation to the selection of the diagnostic techniques and instrumentation in the predictive maintenance programs. The model uses a combination of tools belonging to operational research such as: analytic hierarchy process (AHP) and factor analysis (FA). The model has been tested in screw compressors when lubricant and vibration analyses are integrated.

The continuous production process requires a high degree of availability and the elimination of unexpected breakdown that could cause a prolonged stoppage in production [9]. Predictive maintenance can contribute to improving plant availability, safety, quality, reduction of maintenance costs, etc. This has led to an increase in the number of predictive maintenance programs (PMPs) applied, but, during the setting up of a PMP, there is a number of decisions involved that lack decision support systems or models. This article aims to contribute towards resolving this problem. Although there is a limited number of decision support systems related to predictive maintenance, the following models should be taken into consideration. In Ref. [15], a proportional-hazards model with Weibull baseline hazard function and time-dependent stochastic covariates representing monitored conditions is suggested and a software is developed to assist engineers to optimize decisions. In Ref. [30], Markov models are described for establishing optimum inspection intervals for phased deterioration of monitored complex components in a system with severe down time costs. In Ref. [16], statistical analysis of vibration data is undertaken using a software package to establish the key vibration signals that are necessary for risk estimation. Ref. [19] presents a real-time neural network-based condition monitoring system for rotating mechanical equipment. In Ref. [29], condition predictors of significant items of the system are monitoring taking into account the availability and cost-effectiveness of the monitoring techniques. In this article, a model is presented for the selection of diagnostic techniques and instrumentation in a predictive maintenance program. To construct the model, factor analysis and analytic hierarchy process are combined. The model is applied to screw compressors which are monitored by means of PMPs based on lubricant and vibration analyses and when the aforementioned techniques are applied simultaneously. The layout of the paper is as follows. Section 2 is an introduction to predictive maintenance techniques, lubricant and vibration analyses and the integration of both techniques are presented. Section 3 describes the characteristics of the mathematical tools used in the construction of the decision support model proposed: factor analysis and analytic hierarchy process. Section 4 presents the model for the selection of diagnostic techniques and instrumentation in predictive maintenance. Section 5 describes the application of the model to a screw compressor. Section 6 presents the results obtained from applying the model to a PMP integrating lubricant and vibration analyses. Section 7 presents the conclusions.

In the decision support model designed, technological and organizational issues have been incorporated that until now had not been sufficiently researched in the topic of a predictive maintenance program. Vibration analysis and lubricant analysis are the most frequently applied predictive techniques at present, as a result of which the integration of both techniques in a single predictive maintenance program can provide significant benefits for the company. A model of selection of diagnostic techniques and instrumentation in a predictive maintenance program (MSDT-PMP) has been developed. Factor analysis and AHP have been combined. The model is applied to different technological levels in PMPs based on integrated lubricant and vibration in screw compressors placed in a petrochemical plant. The results obtained will facilitate the decision making of the planner of the predictive maintenance program, as well as favour the development of the integration of predictive techniques, an aspect that currently lacks models for making decisions, due to the technical and organizational difficulties that its application represents, aspects in which this article aims to contribute.