To ensure the normal operation of the system, the enterprise’s operations engineer will monitor the system through the KPI (key performance indicator). For example, web page visits, server memory utilization, etc. KPI anomaly detection is a core technology, which is of great significance for rapid fault detection and repair. This paper proposes a novel dual-stage attention-based LSTM-VAE (DA-LSTM-VAE) model for KPI anomaly detection. Firstly, in order to capture time correlation in KPI data, long–short-term memory (LSTM) units are used to replace traditional neurons in the variational autoencoder (VAE). Then, in order to improve the effect of KPI anomaly detection, an attention mechanism is introduced into the input stage of the encoder and decoder, respectively. During the input stage of the encoder, a time attention mechanism is adopted to assign different weights to different time points, which can adaptively select important input sequences to avoid the influence of noise in the data. During the input stage of the decoder, a feature attention mechanism is adopted to adaptively select important latent variable representations, which can capture the long-term dependence of time series better. In addition, this paper proposes an adaptive threshold method based on anomaly scores measured by reconstruction probability, which can minimize false positives and false negatives and avoid adjustment of the threshold manually. Experimental results in a public dataset show that the proposed method in this paper outperforms other baseline methods.

KPIs (Key Performance Indicators) in distributed systems may involve a variety of anomalies, which will lead to system failure and huge losses. Detecting KPI anomalies in the system is very important. This paper presents a time series anomaly detection method based on correlation analysis and HMM. Correlation analysis is used to obtain the correlation between abnormal KPIs in the system, thereby reducing the false alarm rate of anomaly detection. The HMM (Hidden Markov Model) is used for anomaly detection by finding the close relationship between abnormal KPIs. In our correlation analysis of abnormal KPIs, firstly, the time series prediction model (1D-CNN-TCN) is proposed. The residual sequence is obtained by calculating the residual between the predicted value and the actual value. The residual sequence can highlight the abnormal segment in each data point and improve the accuracy of anomaly screening. According to the obtained residual sequence, these abnormal KPIs are preliminarily screened out from the historical data. Next, KPI correlation analysis is performed, and the correlation score is obtained by adding a sliding window onto the obtained anomaly index residual sequence. The correlation analysis based on the residual sequence can eliminate the interference of the original data fluctuation itself. Then, a correlation matrix of abnormal KPIs is constructed using the obtained correlation scores. In anomaly detection, the constructed correlation matrix is processed to obtain the adaptive parameters of the HMM model, and the trained HMM is used to quickly discover the abnormal KPI that may cause a KPI anomaly. Experiments on public data sets show that the method obtains good results.

Key performance indicator (KPI) anomaly detection is the underlying core technology in Artificial Intelligence for IT operations (AIOps). It has an important impact on subsequent anomaly location and root cause analysis. Variational auto-encoder (VAE) is a symmetry network structure composed of encoder and decoder, which has attracted extensive attention because of its ability to capture complex KPI data features and better detection results. However, VAE is not well applied to the modeling of KPI time series data and it is often necessary to set the threshold to obtain more accurate results. In response to these problems, this paper proposes a novel hybrid method for KPI anomaly detection based on VAE and support vector data description (SVDD). This method consists of two modules: a VAE reconstructor and SVDD anomaly detector. In the VAE reconstruction module, firstly, bi-directional long short-term memory (BiLSTM) is used to replace the traditional feedforward neural network in VAE to capture the time correlation of sequences; then, batch normalization is used at the output of the encoder to prevent the disappearance of KL (Kullback–Leibler) divergence, which prevents ignoring latent variables to reconstruct data directly. Finally, exponentially weighted moving average (EWMA) is used to smooth the reconstruction error, which reduces false positives and false negatives during the detection process. In the SVDD anomaly detection module, smoothed reconstruction errors are introduced into the SVDD for training to determine the threshold of adaptively anomaly detection. Experimental results on the public dataset show that this method has a better detection effect than baseline methods.

A new lateral stability control method, which is based on vehicle sideslip angle and tire cornering stiffness estimation, is proposed to improve the lateral stability of the four-in-wheel-motor-driven electric vehicle (FIWMD-EV) in this paper. Through the lateral tire force information, vehicle sideslip angle can be estimated by the extended kalman filter (EKF). Using the estimated vehicle sideslip angle, tire cornering stiffness can be also estimated by forgetting factor recursive least squares (FFRLS). Furthermore, combining with the vehicle dynamics model, an adaptive control target model is proposed with the information on vehicle sideslip angle and tire cornering stiffness. The new lateral stability control system uses the direct yaw moment control (DYC) based on dynamic sliding mode is proposed. The performance and effectiveness of the proposed vehicle state estimation and lateral stability control system are verified by CarSim and Simulink cosimulation. Comparing with the traditional PI control algorithm, the robustness of the dynamic sliding mode controller has been demonstrated.

In abrasive belt grinding Titanium alloy blade of aviation engine experiment, through the single factor experiment method, the influence of abrasive belt linear speed and work-piece feeding speed on the grinding quantity is discussed. The relationship between the clogging degree of abrasive belt and grinding time is analyzed. Then the influence of abrasive belt linear speed and work-piece feeding speed on the clogging degree of belt is discussed. The results indicate that when grinding time is 3 ~ 30 min or so, the clogging degree of belt does not change significantly. Therefore the grinding process is relatively stable.