## 1 Introduction  
NASA proposed the Integrated Vehicle Health Management Technology (IVHM) in 1992 to enhance spacecraft systems' operation, maintenance, and decision-making activities. With the passage of time, this concept underwent numerous iterations, and in 2020, it evolved into Intelligent Health and Task Management (IHMM), which is the latest technology available today [1]. According to the International Aviation Association (IATA) 2018 report, the aerospace industry invested a whopping $76 billion in vehicle overhauls in 2017 [2]. This highlights the growing significance of fault prognosis and health management technology, which has become a crucial aspect of several industrial sectors, including aerospace. As modern industries continue to evolve and develop, comprehensive health management and diagnosis have become integral to maintaining the system's operational life cycle. The field of prognostic health management is increasingly turning towards deep learning, which is gaining more attention due to its remarkable classification and feature extraction capabilities [3]. The control Moment Gyroscope (CMG) is a vital component of large-scale and long-life spacecraft attitude adjustment systems. It comprises a high-speed rotor system and a servo frame system, which use angular momentum exchange with the spacecraft to execute attitude adjustments. To ensure the smooth operation of CMG, several key operating data, such as rotor motor current, rotor speed, frame servo motor current, frame angle, frame command, and shaft temperature, must be meticulously monitored and controlled. Additionally, the spacecraft attitude and motion data can be used as a reference to assist in determining whether the system is functioning normally. The stable and consistent operation of CMG is critical to the successful completion of the spacecraft's mission. Early troubleshooting is essential, and anomaly detection plays a crucial role in identifying and resolving potential issues in a timely and efficient manner [3, 4, 5]. Health management refers to diagnosing and preventing system failure while predicting the reliability and remaining useful life of its components. In the past few decades, systematic health management research has been a popular direction. Much of the initial research focused on addressing the different failures that occur at the component level and at the system level. The attention of researchers since then has focused on the integration of anomaly, diagnostics, and prediction techniques across systems and related platforms. Fault diagnosis and health management technology has been developed in three directions over the years, namely model-based [6], knowledge [7], and data-driven methods [8, 9, 10, 11]. Compared with methods such as model-based methods or knowledge-based expert systems, data-driven methods do not require the establishment of accurate models, and there is no bottleneck in knowledge acquisition. A data-driven approach is a new paradigm of bottom-up solutions [3]. It can save a lot of manpower and material resources related to knowledge acquisition or accurate model establishment. Therefore, fault prognosis and health management based on deep learning have entered people's field of vision. Deep learning is a field in machine learning, which uses deep artificial neural networks to learn the inherent laws and representation levels of data, and build models for feature extraction and task execution. Anomaly detection based on deep learning can more efficiently and accurately extract anomaly information and prevent early failures from the current large-scale spacecraft telemetry data [12]. The deep learning methods suitable for spacecraft telemetry time series data mainly include Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and so on [13]. Among them, LSTM has the ability to maintain long-term information memory, which has huge advantages in the fields of Natural Language Processing (NLP), speech recognition, and time series prediction [14].