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Fault diagnosis of machines operating under variable conditions requires advanced signal analysis methods. Variable conditions of load, temperature or rotational speed influence the values of vibration signals.
This paper proposes a diagnosis method based on order analysis and an artificial neural network trained solely on data for a machine operating in a fault-free condition. The order spectrum of the new parameter rDPNS, which does not depend on the machine’s working conditions, was proposed. The obtained order spectrum of this parameter allows the identification of faults by the theory of fault diagnostics.
The proposed method has been verified in diagnosing the degradation of a two-stage cylindrical gear. The diagnostic experiment was conducted on a laboratory bench. The signals of vibration acceleration, rotational speed, and current supply to the drive motor were recorded at varying load and temperature.
The results of the experiment conducted on the laboratory bench showed the effectiveness of the proposed method.
