Convolutional neural network with Huffman pooling for handling data with insufficient categories: A novel method for anomaly detection and fault diagnosis.

The rotating component is an important part of the modern mechanical equipment, and its health status has a great impact on whether the equipment can safely operate. In recent years, convolutional neural network has been widely used to identify the health status of the rotor system. Previous studies are mostly based on the premise that training set and testing set have the same categories. However, because the actual operating conditions of mechanical equipment are complex and changeable, the real diagnostic tasks usually have greater diversity than the pre-acquired datasets. The inconsistency of the categories of training set and testing set makes it easy for convolutional neural network to identify the unknown fault data as normal data, which is very fatal to equipment health management. To overcome the above problem, this article proposes a new method, Huffman-convolutional neural network, to improve the generalization ability of the model in detection task with various operating conditions. First, a new Huffman pooling kernel is designed according to the Huffman coding principle and the Huffman pooling layer structure is introduced in the convolutional neural network to enhance the model's ability to extract common features of data under different conditions. Second, a new objective function is proposed based on softmax loss, intra-class loss, and inter-class loss to improve the Huffman-convolutional neural network's ability to distinguish different classes of data and aggregate the same class of data. Third, the proposed method is tested on three different datasets to verify the generalization ability of the Huffman-convolutional neural network in diagnosis tasks with multi-operating conditions. Compared with other traditional methods, the proposed method has better performance and greater potential in multi-condition fault diagnosis and anomaly detection tasks with inconsistent class spaces.

A Novel Framework for Anomaly Detection for Satellite Momentum Wheel Based on Optimized SVM and Huffman-Multi-Scale Entropy.

The health status of the momentum wheel is vital for a satellite. Recently, research on anomaly detection for satellites has become more and more extensive. Previous research mostly required simulation models for key components. However, the physical models are difficult to construct, and the simulation data does not match the telemetry data in engineering applications. To overcome the above problem, this paper proposes a new anomaly detection framework based on real telemetry data. First, the time-domain and frequency-domain features of the preprocessed telemetry signal are calculated, and the effective features are selected through evaluation. Second, a new Huffman-multi-scale entropy (HMSE) system is proposed, which can effectively improve the discrimination between different data types. Third, this paper adopts a multi-class SVM model based on the directed acyclic graph (DAG) principle and proposes an improved adaptive particle swarm optimization (APSO) method to train the SVM model. The proposed method is applied to anomaly detection for satellite momentum wheel voltage telemetry data. The recognition accuracy and detection rate of the method proposed in this paper can reach 99.60% and 99.87%. Compared with other methods, the proposed method can effectively improve the recognition accuracy and detection rate, and it can also effectively reduce the false alarm rate and the missed alarm rate.