Rolling bearing fault diagnosis is one of crucial tasks in mechanical equipment fault diagnosis. Currently, artificial intelligence and machine learning-driven fault diagnosis methods are extensively utilized for rolling bearing. When compared to traditional techniques, the diagnostic accuracy has significantly improved. These methods, however, need a substantial amount of labelled training data, which is difficult to obtain in actual failures. In order to resolve this problem, Transfer Learning (TL) was created to learn in the target domain by accessing knowledge from the pertinent labelled source domain. Inspired by Maximum Mean Discrepancy, this paper puts forward a Convolutional Neural Network (CNN) based Two-layer Transfer Learning (CTTL) method for fault diagnosis. In the first layer, the fault features are automatically extracted by CNN and a term called Feature Weighted Maximum Mean Discrepancy (WMMD) is considered to minimise the difference between source and target domains. In the second layer, the Third Dataset, which is based on the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) method was designed on the basis of the predicted labels from the first layer. The Calinski-Harabaz (CH) index of the Target Dataset controls the iteration times of CTTL. CTTL change the process of Transfer Learning method from learning the distribution of domains to learning the distribution of fault types in more detail, which will get higher accuracy. Proposed CTTL is tested by the bearing datasets of Case Western Reserve University (CWRU) and XJTU-SY. The experimental findings reveal that CTTL is capable of achieving a high diagnosis accuracy across different load domains. In the majority of experiments, CTTL outperformed other algorithms, including Deep Neural Network, Support Vector Machine, and several other methods.

As one of the key components for active compliance control and human–robot collaboration, a six-axis force sensor is often used for a robot to obtain contact forces. However, a significant problem is the distortion between the contact forces and the data conveyed by the six-axis force sensor because of its zero drift, system error, and gravity of robot end-effector. To eliminate the above disturbances, an integrated compensation method is proposed, which uses a deep learning network and the least squares method to realize the zero-point prediction and tool load identification, respectively. After that, the proposed method can automatically complete compensation for the six-axis force sensor in complex manufacturing scenarios. Additionally, the experimental results demonstrate that the proposed method can provide effective and robust compensation for force disturbance and achieve high measurement accuracy.

The fiber optic gyroscope (FOG) is a high precision inertial navigation device, and it is necessary to ensure its reliability for effective use. However, the extracted fault features are easily distorted due to the interference of vibrations when the FOG is in operation. In order to minimize the influence of vibrations to the greatest extent, a fusion diagnosis method was proposed in this paper. It extracted features from fault data with Fast Fourier Transform (FFT) and wavelet packet decomposition (WPD), and built a strong diagnostic classifier with a sparse auto encoder (SAE) and a neural network (NN). Then, a fusion neural network model was established based on the diagnostic output probabilities of the two primary classifiers, which improved the diagnostic accuracy and the anti-vibration capability. Then, five fault types of the FOG under random vibration conditions were established. Fault data sets were collected and generated for experimental comparison with other methods. The results showed that the proposed fusion fault diagnosis method could perform effective and robust fault diagnosis for the FOG under vibration conditions with a high diagnostic accuracy.

An accurate attitude measurement system is crucial for a tunnel boring machine to run along the planned axis. But the accuracy of an attitude measurement system decreases when the tunnel boring machine works under vibration of greater than 9.8m/s². To solve the above problem, a fusion method of a fiber optic gyroscope and an inclinometer is investigated. In order to improve the stability of the measurement system under vibration, the weight adaptive Kalman information fusion method (WKF) is proposed. The method consists of a two-layer information fusion process. The first layer is a variance-weighted pre-processing method, which fuses the fiber optic gyroscope and inclinometer with an adaptive weight to adapt to varying vibration, and the second layer is a Kalman information fusion. Experimental results show that the WKF method can achieve a high measurement accuracy of the attitude angle under both low and strong vibration.

The shield machine is a critical piece of equipment for tunnel construction and necessitates high-precision attitude measurement for ensuring construction quality. During tunneling through hard rocks, the machine produces intense vibrations, which detrimentally affect the precision of attitude measurement instruments, subsequently impacting the accuracy of the machine’s attitude assessment. In this paper, we present a novel approach that introduces a Double-Weighted Information Fusion method to coordinate the capabilities of fiber optic gyroscopes and inclinometers. This method dynamically adjusts two parameters, allocating distinct weights to the posterior estimates of the Kalman Filter (KF) and the original angular velocity values. Additionally, it integrates a Complementary Filter with a KF, aiming to counteract the phase lag introduced by the KF during periods of attitude change. Extensive comparison experiments validate the algorithm’s effectiveness. Experimental results demonstrate that the proposed Double-weighted Fusion algorithm decreases the average angular measurement error by 50% relative to the KF, and by 46.7% compared to the Adaptive Kalman Filter. The proposed method enhances the system’s capability to measure attitude angles. It is well-suited for high-precision attitude measurement of engineering equipment operating under conditions of strong vibrations.