A Spectrogram Based Local Fluctuation Feature for Fault Diagnosis with Application to Rotating Machines

Rotating machines are one of the most common equipment in modern industry, the health condition of the equipment is closely linked to safety of workers and production effectiveness. Thus accurate and robust fault diagnostic approaches are vital to safety production. In practice, diagnostic accuracy is seriously affected by noises, especially in low signal-to-noise (SNR) ratio conditions, and the quality of fault features is positively link to the diagnosing accuracy. In consideration of distinguishable feature expression can improve diagnosing result and robust to wider range of experimental conditions, this paper presents a novel spectrogram based local fluctuation feature (SLFF) for low SNR conditions. Firstly, signals are transformed into spectrograms. Then a feature extracting window bank is established on spectrograms for SLFF. At last, a support vector machine (SVM) is applied as a fault classifier for evaluating the proposed feature. The proposed SLFF represents the basic spectral shape and variation which leads to robust and well distinguishable feature expression, the feature reveals the differences of spectral local variation trends between normal and fault types that can improve the discrimination under the influence of strong noises. The effectiveness of the proposed method has been proved experimentally in this paper.