Coal mine wire rope detection is related to personnel and production safety. With the Chinese coal mining trend tending towards deep mining, a considerable amount of data is critical for the online detection of deep well lifting wire rope. To improve the sampling rate, decrease the
analysis processing time and realise real-time online detection, this paper proposes an online detection data compression processing method. The study focuses on the distortion compression method for the online detection signal of deep well hoisting wire rope. The set partitioning in hierarchical
trees (SPIHT) algorithm is one of the most advanced methods in the field of image transformation coding. Compared with other coding algorithms, the SPIHT algorithm demonstrates desired characteristics such as a high signal-to-noise ratio, lower complexity and decreased computational load,
among others. This paper discusses how, in combination with the image processing method, a compression coding method for the one-dimensional signal of the magnetic leakage detection of the mining wire rope is developed. Furthermore, the set partitioning sorting algorithm is investigated and
analysed, the temporal orientation tree structure of the one-dimensional signal of the wavelet coefficient is defined for wire rope magnetic leakage detection and the SPIHT algorithm is presented, in addition to an example of the one-dimensional signal from the magnetic leakage detection of
the wire rope. The results reveal that under the condition of the normalised mean square error (NMSE; NMSE < 0.01) of distortion, the compression ratio improved by 30%. The online detection signal lossy compression method proposed in this study has a considerable influence on the recovery
of the original signal, in addition to a higher compression ratio and a reduced computation time, compared to the existing method.
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