An LSTM-Autoencoder Architecture for Anomaly Detection Applied on Compressors Audio Data

Abstract

The compressors used in today’s natural gas production industry have an essential role in maintaining the production line operational. Each of the compressors’ components has routine maintenance tasks to avoid sudden failures. Hence, the significant advantages and benefits of performing preventative maintenance tasks in time are decreasing equipment downtime, saving additional costs, and improving the safety and reliability of the whole system. In this paper, anomaly classification and detection methods based on a neural network hybrid model named Long Short-Term Memory (LSTM)-Autoencoder (AE) is proposed to detect anomalies in sequence pattern of audio data, collected by multiple sound sensors deployed at different components of each compressor system for predictive maintenance. In research methodology, this paper has conducted experiments that employed different RNN architectures such as GRU, LSTM, Stacked LSTM, and Stacked GRU with various functions to create a baseline for model evaluation. Each architecture used audio signals dataset received from the compressor system for experiments to consider each neural network model’s performance. According to performance results, an optimal model for anomaly detection with the best performance scores has been proposed in this research. Experiments combined one-dimensional raw audio signal features using SC and Mel spectrogram features were fed to deep learning models to evaluate performance. Hence, such hybrid methods can effectively detect normal and anomaly audio signals collected from a compressor system, increasing the compressor system’s reliability and the sustainability of the gas production line. The combination of multiple-resource features in the proposed hybrid model showed a 100% score in all four-evaluation metrics such as accuracy, precision, recall, and F1 in LSTM-based autoencoder in both test and train results.

Deep learning and acoustic approach for mechanical failure detection in industrial machinery

“…Long short-term memory (LSTM), gate repeating unit (GRU), autoencoder (AE), and convolutional neural network (CNN) are some examples of deep learning algorithms for classification. Long short-term memory (LSTM) is one of the deep learning algorithms that can be used for classification, prediction, and control [8][9][10][11]. It can learn complex patterns and relationships in the input data, making it a valuable tool for a wide range of tasks in various fields such as finance, healthcare, and natural language processing [12][13][14].…”