The data collected from sensors are subject to the presence of anomaly data. These anomalies may stem from sensor malfunctions or poor communication. Prior to the processing of the data, it is imperative to detect and isolate the anomaly data from the substantial volume of normal data. The utilization of data-driven approaches for sensor anomaly detection and isolation frequently confronts the predicament of inadequately labeled data. In one aspect, the data obtained from sensors usually contain no or few examples of faults and those faults are difficult to identify manually from a large amount of raw data. Additionally, the operational states of a machine may undergo alterations during its functioning, potentially resulting in different sensor measurement behaviors. However, the operational states of a machine are not clearly labeled either. In order to address the challenges posed by the absence or lack of labeled data in both domains, a sensor anomaly detection and isolation method using LSTM (long short-term memory) networks is proposed in this paper. In order to predict sensor measurements at a subsequent timestep, behaviors in the preceding timesteps are utilized to consider the influence of the varying operational states. The inputs of the LSTM networks are selected based on prediction errors trained by a small dataset to increase the prediction accuracy and reduce the influence of redundant sensors. The residual between the predicted data and the measurement data is used to determine whether an anomaly has been identified. The proposed method is evaluated using a real dataset obtained from a truck operating in a mine. The results showed that the proposed network with the input-selection method demonstrated the ability to accurately detect drift and stall anomalies accurately in the experiments.
Wu et al. (Fri,) studied this question.