## 1 Introduction  
With the development of sensors, computers and communication technologies, more and more high-speed and high-precision sensors are applied in the field of traffic detection. As a result, the amount of data in traffic database is also growing rapidly [1]. At the same time, due to the inconsistency of the front-end sensors and the changeability of the natural environment, the traffic data obtained contains not only normal traffic information, but also abnormal data caused by electromagnetic interference, detector failure, transmission network failure and traffic accidents--thus affecting the reliability of the intelligent transportation system (ITS) [2]. Therefore, it is of great practical significance to carry out research on the fault data diagnosis technology of traffic detectors to effectively improve the reliability of ITS operations [3]. After combing through the previous work in detail, the methods of data process monitoring can divided into three categories: mathematical model-based, knowledge-based and data-driven methods. In the urban traffic system, the data acquired by the detector is huge and the system dynamics are complex. It is impossible to establish an accurate mathematical model for such a system. Moreover, the knowledge or experience of experts is also subjective. However, the monitoring process data contains abundant process information, and the operation status of the system can be characterized by the normal or abnormal data. It is pointed out in the literature [4] that research is gradually changing from a model-based approach to a data-driven approach. Literature [5] divides data-driven monitoring methods into machine learning, signal processing, and multivariate statistics. Machine learning methods aim at the correct rate of fault diagnosis [6]. They train machine learning algorithms such as neural networks [7] or support vector machines [8] to diagnose faults by using historical data of objects in normal and various fault situations. It has a wide range of applications, but, because of the need for sample data in case of process faults, and the accuracy of the algorithm being closely related to the completeness and representativeness of the samples, it is difficult to be used in those processes that can not obtain a large amount of fault data. Signal processing methods use signal processing methods such as spectral analysis and wavelet transform to analyze the time-frequency characteristics of measured signals, and then diagnose faults [9]. The multivariate statistical analysis method is represented by principal component analysis (PCA). PCA is the core of data-driven process control and fault diagnosis [10]. It constructs a new set of latent variables and forms a new mapping space based on the original data space. The main change information is extracted from it and the statistical features are extracted, so that the spatial characteristics of the original data can be tabulated and the spatial dimension of the original data can be reduced. At the same time, the projection space statistical eigenvectors are orthogonal to each other, which eliminates the correlation between variables and greatly reduces the complexity of the original process characteristic analysis. Given their importance, the fault data detection of traffic detectors is always a research focus and has attracted the attention of numerous scholars for years [11]. At present, the research of traffic detector fault data recognition mainly focuses on data fault identification based on a traffic flow three-parameter rule [12], data fault identification based on statistical analysis and data fault identification based on artificial intelligence [13]. [PERSON] [14], by analyzing the influence of inner relationship of data acquisition interval and three traffic flow parameters, then designed a four-step data sieving method.