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Development of a State-related Evaluation for Diagnostic-oriented Data Filtering Approach



Ensuring the total reliability and availability of complex systems such as safe mechatronic systems or cost-sensitive machine components is of increasing importance. To monitor such systems during operation, parameters subjected to several influences affecting the efficiency, functionality, and safety are measured. It is of major interest to infer the actual State-of-Health of critical components from acquired data. The supervision of critical components of mechatronic systems becomes an important task and will be discussed here. The degree of wear and the functionality of complex systems are significant for the system reliability. The overall reliability of technical systems must be ensured in order to reduce the risks and costs of a system failure. To classify the machine state using easy-to-measure signals, two aspects are important: the filtering and the interpretation of the measured data. Core of this contribution is the development and application of a state-related evaluation for diagnostic-oriented data filtering approach to be used directly with industrial data or measurements from technical systems in operation. The data used in this contribution are taken from experiments generated using a wear test rig. The test rig designed for evaluation of tribological effects consists of two wear plates which interact with each other. A connection between measured macroscopic data (sliding force equivalent data resulting from test rig’s operation) and the degree of wear of the lubricated surface is established to calculate information about the state of a sliding surface between the two machine parts. The time behavior of the pressure is taken using arithmetic mean value and sliding window technique. The filtering method is explained in previous work of the authors [1]. The actual level of wear, calculated using the integrated damage increments, and the actual changes of the level of wear are utilized to define three states (healthy/good condition; small changes in condition; non healthy/not in good condition) heuristically. This concept is briefly introduced in [1]. A comparison of different measurements with the same operating conditions shows similar results.

doi: 10.12783/SHM2015/76

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