Abstract
To promote the progress of fault diagnosis system, this study proposes an intelligent and effective fault diagnosis algorithm based on data-driven. Firstly, we propose a new time-frequency analysis method named second-order time-reassigned multisynchrosqueezing transform (STMSST) based on Gaussian-modulated linear group delay (GLGD) model to deal with the vibration signals of fault object for obtaining time-frequency images with high resolution. Then, an improved training method named evenly mini-batch training method is combined with convolutional neural network (CNN) to train and learn fault features from those obtained time-frequency images. Further, the proposed fault diagnosis algorithm is tested on the Case Western Reserve University (CWRU) bearing dataset and the Machinery Failure Prevention Technology (MFPT) Society dataset, respectively, and the experimental results indicate that the feature representation and training effect in our method is superior to state-of-the-art fault diagnosis methods. Finally, the proposed method is applied on the loudspeaker pure-tone detection dataset, which achieves the loudspeaker anomaly diagnosis, and the diagnosis result has verified that the method can meet the needs of practical engineering.
Highlights
In modern industry, advanced condition monitoring and fault diagnosis technologies detect the early faults of equipment to avoid the occurrence of accidents, and fundamentally solve the problems of insufficient maintenance and excessive maintenance [1]
Because mechanical components are widely used in life, the fault diagnosis of components is as a judgment basis of the health condition
We combine iteration operation and synchrosqueezing operation based on time reassignment to promote the progress of time-frequency analysis method as the signal-to-image conversion tool, and the new method is called second-order time-reassigned multisynchrosqueezing transform (STMSST) based on Gaussian-modulated linear group delay (GLGD) model
Summary
In modern industry, advanced condition monitoring and fault diagnosis technologies detect the early faults of equipment to avoid the occurrence of accidents, and fundamentally solve the problems of insufficient maintenance and excessive maintenance [1]. Chen et al [15] proposed a DCNN based on data fusion method to supplement the fault features of the raw vibration signals, which fused the horizontal and vertical vibration signals These methods obtain fault samples without manual processing, but the structures of these CNN models are more complex. Fourer and Auger [22] proposed second-order time-reassigned synchrosqueezing transform (STSST) and He et al [23] applied Gaussian-modulated linear group delay (GLGD) model to the STSST These two time-frequency analysis methods are based on time reassignment to operate synchrosqueezing. We combine iteration operation and synchrosqueezing operation based on time reassignment to promote the progress of time-frequency analysis method as the signal-to-image conversion tool, and the new method is called second-order time-reassigned multisynchrosqueezing transform (STMSST) based on GLGD model. N affects the ability of energy concentration and cross-term suppression in the time-frequency image, and Ts(t, η) in (1) is denoted by Ts[1](t, η)
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