Detection of damaged mechanical components in their early stages is crucial in many applications. The diagnostics of mechanical components is achieved most effectively using vibration and/or acoustical measurements, sometimes accompanied by oil debris indications. The paper describes a concept for fusion and decision for mechanical components, based on vibroacoustic signatures. Typically in diagnostics of complex machinery, there are numerous records from normally operating machines and few recordings with damaged components. Diagnostics of each mechanical component requires consideration of a large number of features. Learning classification algorithms cannot be applied due to insufficient examples of damaged components. The proposed system presents a solution by introducing a hierarchical decision scheme. The proposed architecture is designed in layers imitating expert’s decision reasoning. The architecture and tools used allow incorporation of expert’s knowledge along with the ability to learn from examples. The system was implemented and tested on simulated data and real-world data from seeded tests. The paper describes the proposed architecture, the algorithms used to implement it and some examples.
How to Cite
decisioning, diagnostic algorithm, feature extraction, vibration analysis, bearing fault detection, fusion
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