Early fault or unusual behavior detection can reduce the risk of equipment failure improve performance and increase safety. Anomaly detection in industrial big data involves identifying deviations from normal patterns in large-scale datasets. This method assists in preventing equipment failures optimizing maintenance schedules and raising overall operational efficiency in industrial settings by identifying anomalous behaviors or outliers. Through the utilization of deep learning procedures, this investigation endeavours to apply are fined procedure for anomaly detection in industrial big data. Pre-processing, feature selection and Anomaly detection are three steps of a process that are used. The input data is first fed into MapReduce framework where it is divided and pre-processed. Imputation of missing data and Yeo-Jhonson transformation are then applied to eliminate noise from data. After pre-processed data is generated, it is put through a feature selection phase using Serial Exponential Lotus Effect Optimization Algorithm (SELOA). The algorithm is created newly by combining Lotus Effect Optimization Algorithm (LOA) with Exponential Weighted Moving Average (EWMA). Finally, anomaly detection is done using the features that are selected by means of Deep Belief-MobileNet1D, which combines MobileNet1D and Deep Belief Network (DBN). With a recall of 96.2 %, precision of 92.8 %, F1 score of 94.5 % and accuracy of 95.9 %, results show that the proposed strategy surpasses standard approaches. These findings demonstrate Deep Belief-MobileNet1D model's ability to detect anomalies in industrial big data.
