Electromagnetic testing is the most widely used technique for the inspection of steel wire ropes. As one of the electromagnetic detecting approaches, the magnetic flux leakage (MFL) method has the best effect for the detection of broken wires. However, existing sensors based on MFL method still have some problems. (1) The size of the permanent magnet exciter is usually designed according to experience or rough calculation, and there is not enough depth analysis for its excitation performance; (2) Since the detectable angular range for a single Hall component is limited, Hall sensor arrays are often employed in the design of MFL sensors, which will increase the complexity of the subsequent signal processing due to the extensive use of Hall components; (3) Although the new magneto-resistance sensor has higher sensitivity, it is difficult to be applied in practice because of the requirement of the micron-level lift-off. To solve these problems, a sensor for the detection of broken wires of steel wire ropes based on the principle of magnetic concentration is developed. A circumferential multi-circuit permanent magnet exciter (CMPME) is employed to magnetize the wire rope to saturation. The traditional Hall sensor array is replaced by a magnetic concentrator to collect MFL. The structural parameters of the CMPME are optimized and the performance of the magnetic concentrator is analyzed by the finite element method. Finally, the effectiveness of the designed sensor is verified by wire breaking experiment. 1–5 external broken wires, handcrafted on the wire rope with a diameter of 24 mm, can be clearly identified, which shows great potential for the inspection of steel wire ropes.

Each pattern recognition method has its advantages and disadvantages to diagnose the state of rotating machinery. There are many fault types of rolling bearings with apparent uncertainty. The optimal fusion level is usually challenging to be selected for a specific fault diagnosis task, and extensive human labour and prior knowledge are also highly required during these selections. To solve the above problems, a multimodel decision fusion method based on Deep Convolutional Neural Network and Improved Dempster-Shafer Evidence Theory (DCNN-IDST) is proposed for the inspection of rolling bearing. To solve the defect of the original evidence theory method in the fusion of high-conflict evidence, the fuzzy consistency matrix is introduced. By calculating the factor weight, the reliability and rationality of D-S evidence theory are improved. The DCNN model can learn features from the original data and carry out adaptive feature extraction for multiple sensor information. The features extracted by DCNN adaptively are input into multiple network models for decision fusion. The new method of DCNN-IDST multimodel decision fusion is applied to detect the damage of rolling bearings. To evaluate the effectiveness of the proposed method, both the BP neural network and RBF neural network are used to set up a multigroup comparison test. The result demonstrates that the proposed method can detect the fault of the rolling bearing effectively and achieve the highest diagnosis accuracy among all the tested methods in the experiment.