Kari Ilmari Saarinen

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This work presents a methodology identifying the increase of backlash amount in any type of gearboxes for a movable mechanical unit. No external sensor is required. A dedicated test cycle, of at least 4–8 seconds long, is run regularly on the axis under examination. The motion data, i.e. position, velocity and torque references is logged and stored(More)
This paper presents a data-driven approach to diagnostics of systems that operate in a repetitive manner. Considering that data batches collected from a repetitive operation will be similar unless in the presence of an abnormality, a condition change is inferred by comparing the monitored data against an available nominal batch. The method proposed(More)
This paper presents a method for condition monitoring of systems that operate in a repetitive manner. A data-driven method is proposed that considers changes in the distribution of data samples obtained from multiple executions of one or several tasks. This is made possible with the use of kernel density estimators and the Hellinger metric between(More)
Fault detection algorithms (FDAs) process data to generate a test quantity. Test quantities are used to determine presence of a fault in a monitored system, despite disturbances. Because only limited knowledge of the system can be embedded in an FDA, it is important to evaluate it in scenarios relevant in practice. In this paper, simulation based approaches(More)
This paper presents a method for monitoring of systems that operate in a repetitive manner. Considering that data batches collected from a repetitive operation will be similar unless in the presence of an abnormality, a condition change is inferred by comparing the monitored data against a nominal batch. The method proposed considers the comparison of data(More)
This paper presents a method for monitoring of systems that operate in a repetitive manner. Considering that data batches collected from a repetitive operation will be similar unless in the presence of an abnormality, a condition change is inferred by comparing the monitored data against a nominal batch. The method proposed considers the comparison of data(More)
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