The abnormal signal is inevitable in big data acquired from the harsh industrial environment. Abnormal data detection is an important part of condition monitoring for rotating parts and is also the premise of data cleaning, compensation, and mining. To detect abnormal data segments of rolling bearings, this paper proposes a dynamic adaptive local outlier factor (DALOF) anomaly detection method. A data dynamic segmentation method based on sliding windows is first designed to obtain samples with variable lengths. Then for reducing the feature space discrepancy, a time-domain feature extract and fusion method based on principle component analysis (PCA) is exploited. To improve the accuracy of abnormal data detection, a data quality evaluation model is established to assess each data segment by using the DALOF. Also, the validity of the proposed method is verified by analyzing the signal including missing data, random interference data, and drift data. Several other methods are respectively employed to identify these abnormal data to further show the benefits of the developed methodology.

Missing data caused by many factors such as equipment short circuits or data cleaning affect the accuracy of rotating machinery condition monitoring. To improve the precision of missing data recovery, a compressed sensing (CS)-based vibration data repair method is developed. Firstly, based on the Gaussian random matrix, an improved optimized measurement matrix (OMM) is proposed to accurately sample data. Then a sparse representation of the vibration signal through discrete cosine transform (DCT) is utilized to sparse the noisy vibration signal. Finally, the orthogonal matching pursuit (OMP) algorithm is employed to reconstruct the missing signal. The effectiveness of the proposed method is verified by analyzing constant and variable speed time series of rolling bearings. Compared with other data repair methods, it is highlighted that OMM has a higher repair precision at different loss rates.

Anomaly data detection is not only an important part of the condition monitoring process of rolling element bearings, but also the premise of data cleaning, compensation and mining. Aiming at the abnormal data segment detection of the vibration signals of a rolling element bearing, this paper proposes an abnormal data detection model based on comprehensive features and parameter optimization isolation forest (CF-POIF), which can adaptively identify abnormal data segments. First, in order to extract the mutation feature of vibration signals more accurately, the concept of comprehensive feature is proposed, which integrates the time domain and wavelet packet energy features. Then, the particle swarm optimization (PSO) algorithm is used to optimize the rectangular window length and sub sample set capacity in the isolation forest for anomaly detection. Finally, three real cases concerning abnormal data are used to verify the effectiveness of the proposed method. The results demonstrate that the proposed method is able to detect missing data, drift data and external interference data effectively, and it has a higher F1 score and accuracy compared to other methods.