In the field of hot rolling process monitoring, the activation of non‐linear dynamic behaviour may render the procedure of fault diagnosis more difficult. Principal component analysis (PCA) is known as a popular method for diagnosis but as it is basically a linear method, it may pass over some useful non‐linear features of the system behaviour. One possible extension of PCA is kernel PCA (KPCA), owing to the use of non‐linear kernel functions that allow introduction of non‐linear dependences between variables. The objective of this study is to address the problem of fault diagnosis (in terms of non‐linear activation) in hot rolling automation system using a KPCA‐based method. The detection is achieved by comparing the subspaces between the reference and a current state of the system through the concept of subspace angle. It is shown in this work that the exploitation of the measurements in the form of KPCA can effectively improve the detection results.

The scrap falling state in the shearing process of a Wide Heavy Plate Mill depends on manual monitoring and judgment. The steel plate can only move forward after the scrap falls, otherwise, the equipment will be damaged. An experienced operator can use the sound they hear to estimate the falling state. To automatically recognize sound events, a scheme of sound data acquisition processing and recognition for monitoring the scrap state of the shearing process is proposed in this paper. Firstly, a pickup is installed at the monitoring point of the crop shear to collect the sound. Afterward, the sound data is processed and finally fed into the model for recognition. Since many sound segments in the field are not related to key actions, an endpoint detection technique based on a short-term energy threshold and the variable window length strategy is applied in the data processing section to obtain the sound segments to be identified. The Log-Mel spectrogram feature of the sound segment is then extracted. After a data filling to ensure a uniform data shape, the feature map is fed into a lightweight deep convolution neural network called MobileNetV2 to quickly identify sound events. For the shearing process events, the model recognition accuracy can reach 95% of the F1 score in a realistic noise environment and we can identify the scrap falling 0.3s after the action starts. Finally, the model was applied to the UrbanSound8K dataset and achieved 95% recognition accuracy, validating the performance of the method.

Accurate prediction of the rolling force is critical to assuring the quality of the final product in steel manufacturing. Exit thickness of plate for each pass is calculated from roll gap, mill spring, and predicted roll force. Ideal pass scheduling is dependent on a precise prediction of the roll force in each pass. This paper will introduce a concept that allows obtaining the material model parameters directly from the rolling process on an industrial scale by the uniform differential neural network. On the basis of the characteristics that the uniform distribution can fully characterize the solution space and enhance the diversity of the population, uniformity research on differential evolution operator is made to get improved crossover with uniform distribution. When its original function is transferred with a transfer function, the uniform differential evolution algorithms can quickly solve complex optimization problems. Neural network structure and weights threshold are optimized by uniform differential evolution algorithm, and a uniform differential neural network is formed to improve rolling force prediction accuracy in process control system.

This study introduces the technical specifications and the technological process of the aluminium single‐stand 4‐high cold rolling mill, and analyses the mathematical model of the rolling process. The automation system adopts 3‐level computer control, which actuators and sensors (Level 0), basic automation level (Level 1) and process automation level (Level 2), to realise the network data tracking, dynamic adaptive rolling procedure setting, self‐learning model and automatic control of the whole rolling process. The computer control system described has been in full commercial operation after a large number of functional and performance tests. The effectiveness of 3‐level control concept was verified to ensure the quality of the product, and to meet the expected goal of designing.