Sensor-based Condition Monitoring and Predictive Maintenance—An Integrated Intelligent Management Support System

To maintain a competitive edge, it becomes increasingly important for companies to manage their maintenance dollars effectively. This study reveals that a surprisingly large part of capital investment is associated with equipment maintenance in real business environments. Moreover, equipment availability also affects productivity, profitability and the ability to response to market demand. Therefore, maintenance management is becoming an important business area in many industrial settings. Sensory data utilization and interpretation is vital in the decision-making process for maintenance management. The paper proposes that indicative information and early warning about the health of the constituent components of a system are gathered through appropriate sensor measurement and monitoring. This strategy does not specify the use of fixed intervals for maintenance but dynamically adapt the maintenance intervals to the system's actual need for service. This strategy is called condition-based predictive maintenance, in contrast to scheduled maintenance with fixed intervals. Technologies including sensor measurement, data processing, knowledge-based intelligent systems and software implementation are integrated in this research to provide solid support for maintenance management. The purpose of this study is to investigate this new paradigm, its implications in business settings, and its implementation as a software system. Preventing catastrophic failures and damage to equipment is a major objective in this research. Adding value, reducing maintenance costs, and increasing the company's competitiveness are other driving factors for establishing a computerized management support system.