Compression methods for mechanical vibration signals: Application to the plane engines

Abstract

A novel approach for the compression of mechanical vibration signals is presented in this paper. The method relies on a simple and flexible decomposition into a large number of subbands, implemented by an orthogonal transform. Compression is achieved by a uniform adaptive quantization of each subband. The method is tested on a large number of real vibration signals issued by plane engines. High compression ratios can be achieved, while keeping a good quality of the reconstructed signal. It is also shown that compression has little impact on the detection of some commonly encountered defects of the plane engine.

A divide-and-compress lossless compression scheme for bearing vibration signals in wireless sensor networks

“…But the DWT is more suitable only for non-stationary vibration signal compression [15,20]. In our applications, the bearing vibration signals do not exhibit a strong non-stationary characteristics, thus the DCT achieves a higher degree of energy concentration, which is more advantageous to enhance the compression performance.…”

“…Several studies regarding the bearing vibration lossy data compression in remote condition monitoring system have been done. Some of those include using wavelet transform to compress vibration signals [11]; using empirical mode decomposition (EMD) to decompose and compress vibration data first, then applying differential pulse code modulation (DPCM) to further compress the signal after down-sampling [12]; adopting modified discrete cosine transform based compression scheme to compress vibration signals, and doing some improvements to make it suitable for wireless nodes [13]; using the ensemble empirical mode decomposition (EEMD) whose parameters are optimized to extract the intrinsic mode function relating to bearing fault, and this component instead of the original signal is compressed and transmitted to reduce the data transmission [14]; adopting suitable orthogonal transforms to implement sub-band decomposition, and each sub-band is compressed by a uniform adaptive quantization [15]. These bearing vibration lossy data compression methods above achieve very high compression performance, retain the essential machine defect characteristics for fault diagnosis, and perform brilliantly in some applications.…”

A Two-Stage Compression Method for the Fault Detection of Roller Bearings

“…Since much fault information is carried by the vibration signals, vibration-based diagnostic techniques have become the most commonly used and effective method for the fault diagnosis of roller bearings [5][6][7]. It is well known that the vibration-based fault diagnosis of roller bearings can be broadly classified into three categories, namely, time-domain analysis, frequency-domain analysis, and time-frequency analysis [8][9][10].…”

Compressed sparse time–frequency feature representation via compressive sensing and its applications in fault diagnosis

“…Some techniques have been recently proposed to compress signals in time domain based on a transform approach. Marius et al proposed compression method for mechanical vibration signals which was based on the orthogonal transform decomposition into a large number of subbands [7]. Guo et al developed a signal compression method based on the optimal ensemble empirical mode decomposition for bearing vibration signals [8].…”