Early detection of damage to wire ropes used in coal mine winching and traction systems is important for avoiding production delays and for the safety of personnel entering the mine. This study proposes a new magnetic aggregation bridge detection method utilizing a magnetic resistance sensor array, which addresses the shortcomings of present methods for testing the integrity of wire ropes used in coal mining operations. First, the structure and types of damage incurred by such ropes are analyzed. The detection principle of the proposed method and the optimization of magnetic induction strength regions are then described. The selection of three types of magnetic sensitivity detection elements are compared and analyzed. On this basis, the magnetic aggregation method for inspecting wire rope, based on magnetic resistance sensor array, is studied in detail, including the detection effect of including or omitting a magnetic aggregation device. Different structures and sizes of magnetic aggregation devices are examined for their performance effect, showing measures to improve the effect of magnetization detection and selection methods for the magnetic resistance sensor.A detection method using a magnetic resistance sensor array combination matched with the magnetic field focusing model was discussedin detail. Finally, the proposed method is verified experimentally. The results show that the prototype sensor based on the proposed method can clearly detect multiple types of damage to wire rope, achieving a maximum signal-to-noise ratio of 60 dB and a considerable detection effect, compared to the conventional magnetic flux leakage defect detection sensor.

Steel wire ropes are used for transporting personnel and materials in coal mines. Its quality directly affects the safety of mining operation and hence, it is important to accurately identify the defects. Since the electromagnetic detection methods are affected by large amounts of noise, more precise strategies are required. This paper proposes a noise reduction method based on K-singular value decomposition (K-SVD) optimized double-tree complex wavelet transform (DTCWT). Firstly, the discrete coefficients of the evaluation index of different decomposition levels in DTCWT are calculated to obtain the optimal decomposition levels. Afterwards, K-SVD is used to update the sparse dictionary. The decomposed high-frequency modes were optimized for noise reduction. Finally, DTCWT was used to reconstruct the damage detection signal from the wire rope, after noise reduction. The experimental results show that the signal to noise ratio obtained using the proposed method is 30% higher than the other algorithms. The smoothing coefficient obtained is lower than 0.01, whereas the correlation coefficient is found to be more than 0.95, which indicates that the proposed algorithm can effectively reduce the noise in the detection signal and significantly retain its original characteristics, thereby improving the safety of the wire rope.

As a common load-bearing component, mining wire rope produces different types of damage during a long period of operation, especially in the case of damage inside the wire rope, which cannot be identified by the naked eye, and it is difficult to accurately detect such damage using the present technology. In this study we designed a non-destructive testing device based on leakage magnetism, which can effectively detect the internal defects of wire rope damage, and carried out simulation analysis to lay a theoretical foundation for the subsequent experiments. To address the noise reduction problem in the design process, a variational mode decomposition–adaptive wavelet thresholding noise reduction method is proposed, which can improve the signal-to-noise ratio and also calculate the wavelet energy entropy in the reconstructed signal to construct multi-dimensional feature vectors. For the quantitative identification of system damage, a particle swarm optimization–support vector machine algorithm is proposed. Moreover, based on the signal following the noise reduction step, seven different feature vectors, namely, the waveform area, peak value, peak-valley value, wavelet energy entropy classification, and identification of internal and external damage defects, have been determined. The results show that the device can be used to effectively identify internal damage defects. In addition, the comparative analysis showed that the algorithm can reduce the system noise and effectively identify internal and external damage defects with a certain superiority.

This study presents the optimal design of a magnetic excitation model for developing a nondestructive sensor for coal mine hoist wire ropes. The model was established using axial-symmetry finite-element analysis and calculations. The influence of the excitation device parameters on the local magnetization effect of the wire rope was investigated in detail using the axial-symmetry finite-element model. The excitation model parameters of the sensor were optimally designed using a combination of finite-element analysis and an optimization method. The experiments were performed to measure the leakage flux and evaluate the performance of the optimally designed sensor. The results show that the sensor based on the newly designed excitation model can not only improve the signal-to-noise ratio for defect detection in a coal mine hoist wire rope by 11% compared to an existing sensor but also reliably detect small defects with a high detection speed (5 m/s) along the length of the coal mine wire rope.

The testing of wire rope is vital in ensuring personnel safety during coal mine production. At present, it has proven difficult to successfully pre-treat wire rope detection signals, leading to recognition errors and other issues. To this end, this paper details a proposed application of the morphological non-sampling wavelet method to the online detection signal processing of coal mine wire rope. Based on existing mathematical morphological theory, morphological filtering and morphological non-sampled wavelet construction methods, this paper constructs a morphological non-sampling wavelet method suitable for the online detection of the signal characteristics of mine wire rope. This method is then applied to signal preprocessing. The experimental results show that the developed method can effectively filter out noise such as baseline drift, that the signal-to-noise ratio (SNR) of the processed data is 39 dB > 30 dB and the elapsed time is 2 s. The SNRs obtained using the existing wavelet transform method and the morphological filtering method are 17 dB and 22-30 dB, respectively, with elapsed times of 1.99 s and 1.97 s, respectively. In this paper, the effective filtering of the signal is realised under the condition that the processing time of the signal preprocessing method shows no obvious increase.

Hall-effect sensors are used for the non-destructive testing of wire ropes owing to their low power consumption and high operation frequency. The high-speed operation of wire ropes causes vibration inclination at different frequencies, which makes it difficult to detect the ropes. Considering that the radial signal in the magnetic flux leakage (MFL) detection method can respond to damages to the maximum extent possible, this study proposes a radial magnetic concentrator suitable for the non-destructive testing of wire ropes based on theoretical analysis and transient magnetic field simulations. The concentrator improves the radial magnetic circuit, polymerizes the leakage of the magnetic field in the detection device, and the leakage of the magnetic field of the defect converges at the sensor position of the circumferential array to improve the signal-to-noise ratio of the Hall-effect sensor. In addition, the MFL field is homogenized through the structure of the magnetic concentrator when the wire rope is tilted, which weakens the influence of the vibration tilt of the wire rope on the test results. Finally, the experiments show that the amplitude of the wire-rope damage signal is effectively improved by using the proposed radial magnetic concentration technology, hence being convenient for defect analyses.