# The CIPCA-BPNN Failure Prediction Method Based on Interval Data Compression and Dimension Reduction  
[PERSON]  
1 School of Economics and Management, Beihang University, Beijing 100191, China; [EMAIL_ADDRESS] (L.Y. ); [EMAIL_ADDRESS] (G.J. ); [EMAIL_ADDRESS] (F.W.) [PERSON]  
1 School of Economics and Management, Beihang University, Beijing 100191, China; [EMAIL_ADDRESS] (L.Y. ); [EMAIL_ADDRESS] (G.J. ); [EMAIL_ADDRESS] (F.W.) [PERSON]  
1 School of Reliability and Systems Engineering, Beihang University, Beijing 100191, China; changwewbing@  
method uses all the information that the UAV system can collect. After using the machine learning method, it does not rely on the original model of the system to judge the fault [12]. The construction of data-driven methods usually includes three steps: first, collecting fault signals; second, extracting fault features; third, identifying and predicting fault [13]. Because the airborne equipment of UAVs record a large amount of flight parameter data in real time, feature extraction and dimensionality reduction of the flight data are very important tasks. Flight data are real-time flight data collected by onboard sensors, which are a type of signal data. Traditional signal data feature extraction methods including wavelet packet transform (WPT), empirical mode decomposition (EMD), and local mean decomposition (LMD). WPT is a signal feature extraction method that provides local features in the time domain and frequency domain and recognizes sudden components of vibration signals. It is an effective method for processing nonlinear and non-stationary signals [14; 15]. EMD is an adaptive processing method suitable for analyzing nonlinear and non-stationary signals. The algorithm is based on the local characteristic time scale of the signal and has the ability to adaptively decompose the complex signal into multiple independent modal functions [16]. LMD is also an adaptive signal processing method used to adaptively de-compose nonlinear and non-stationary vibration signals into a series of product functions [17]. WPT needs to determine the decomposition scale, so it is not an adaptive signal data processing method and is not conducive to processing big data [18]. EMD can adaptively determine the resolution of the signal in different frequency bands, but the modal mixing problem often occurs [19]. LMD and EMD have some similarities, but LMD is better than EMD in the processing of local signal features [20]. These methods are often used in the field of fault diagnosis [21; 22; 23; 24], but they have some problems. First, extracting data features from the time and frequency domains will destroy the structure of the data itself. Second, these methods will increase the number of variables and cannot achieve the purpose of dimensionality reduction. Principal component analysis based on interval data can solve the problems of compressed data, feature extraction, and dimensionality reduction at the same time. In 1988, [PERSON] proposed symbolic data analysis (SDA), which has been widely used in various fields [25]. Interval data are typical symbolic data, which express a range between the upper and lower bounds. Compared with discrete data, interval data can grasp the internal structural characteristics of data objects globally, which is more conducive to revealing the rules implicit in the data. Therefore, interval data can represent the uncertainty and variability of data and have important application value in decision support. [PERSON], [PERSON], and [PERSON] (2012) proposed the complete-information-based principal component analysis (CIPCA), which can capture the complete information of the data interval and find the meaningful structural information hidden in large-scale data. It is a more efficient method for dimensionality reduction of large-scale numerical data [26]. The interval data principal component method can distinguish fault types more accurately than the traditional principal component analysis method [27].