Risk And Condition Based Intelligent Maintenance Decision-making Optimization System And Application Research

Equipment management in Chinese process industry mostly belongs to the traditional breakdown maintenance pattern, and the basic inspection/maintenance decision-making is insufficient. Equipment inspection/maintenance tasks are mainly based on empirical or qualitative methods, which usually lack identification and classification of critical equipment, so that maintenance resources can't be reasonably allocated. Reliability, availability and safety of equipment are difficult to control and guarantee due to the existing maintenance deficiencies, maintenance surplus, potential danger and possible accidents. In order to ensure stable manufacturing and reduce operation cost, a risk & condition based equipment intelligent maintenance and decision-making optimization system is established in this paper, which utilizes risk & condition based equipment integrity management system as the architecture, and integrates ERP, MES (Manufacturing Executive System), RBM (Risk Based Maintenance)and PMIS (Predictive Maintenance Information System) through IOT (Internet Of Things) and SOA. This system can provide dynamic risk rank data, predictive maintenance information data, equipment performance index data, reliability prediction data and equipment residual life data, thus making personnel at all levels master equipment risk rank and optimized maintenance tasks in time and providing scientific support to maintenance decision-making. The main contents in the paper include:(1) Research on risk-based maintenance and software developmentAccording to the characteristics of equipment management in process industry such as petroleum refining and petrochemical enterprises, this paper investigates relevant risk-based maintenance risk evaluation methods, develops risk-based maintenance (RBM) software and establishes risk-based maintenance and decision-making model.(2) Research on risk & condition based intelligent maintenance and decision-making system optimizationUsing IOT, a risk & condition based intelligent maintenance and decision-making system is set up. Meanwhile, PMIS, MES and RBM modules are integrated on the basis of SOA by adopting computer technology, interface technology, database technology and cable/wireless technology, so that the equipment intelligent maintenance and decision-making platform (characterized by "risk management as the core, professional management as the main line") is formulated. This platform can provide predictive maintenance and decision-making indicator, dynamic risk rank indicator, key performance indicator as well as quantitative analysis data.(3) Research on risk-based dynamic maintenance evaluation and equipment management performance indexFor specific equipment types, data collection and data exchange of reliability data and maintenance data are researched; for the management characteristics in process industry equipment, dynamic risk variation influence factors (including management factor and individual equipment modifying factor) and dynamic risk evaluation techniques are investigated; on the basis of failure data and maintenance data, equipment performance indicator evaluation system (comprising equipment level, device level and company level), equipment management performance indicator decision-making model and performance indicator reliability prediction model are all studied.(4) Research on risk & condition based intelligent maintenance task optimizationUsing Weibull Distribution probability analyzing tool, analysis of MTBF (mean time between failure) and reliability data are carried out, thus realizing reliability prediction; using PCA (Principal Component Analysis), equipment failure features are determined; moreover, we trace the degradation trend of failure features by neural network, grey theory, curvilinear regression and time series modeling, thus realizing equipment residual life prediction; equipment maintenance content and maintenance period are optimized thanks to reliability prediction and residual life prediction; based on dynamic risk analysis and predictive maintenance and decision-making indicator model, management performance decision-making indicator model, equipment reliability prediction model and residual life prediction model, with risk & condition based intelligent maintenance and decision-making system as the platform, a risk & condition based intelligent maintenance task optimization system is established.(5) Engineering application of risk & condition based intelligent maintenance and decision-making systemThe system combines the existing ERP, MES, EAM and PMIS resources, and pays special attention to many aspects. Firstly, it considers traditional equipment management status, and introduces advanced RBM, RBI and SIL risk management techniques. Secondly, it takes into account the lack of reliability data and maintenance data in quantitative risk analysis, and gives attention to the importance of setting up management performance indicator. Thirdly, it emphasizes that the system should have the ability to realize intelligent decision-making based on dynamic risk rank indicator, preventive maintenance indicator, management performance indicator and residual life prediction indicator model, while advocating firm support from the leaders and continual training and education, which are important to the successful application of the system. The engineering application case in Petrochina Jinzhou Petrochemical shows that the establishment of the risk & condition based intelligent maintenance and decision-making system has brought about positive effect on the reliability, availability and safety of the equipment, lowering failure frequency, minimizing failure consequence and reasonably allocating maintenance resources.