LSTM-Autoencoder for Vibration Anomaly Detection in Vertical Carousel Storage and Retrieval System (VCSRS)

Abstract

Industry 5.0, also known as the “smart factory,” is an evolution of manufacturing technology that utilizes advanced data analytics and machine learning techniques to optimize production processes. One key aspect of Industry 5.0 is using vibration data to monitor and detect anomalies in machinery and equipment. In the case of a vertical carousel storage and retrieval system (VCSRS), vibration data can be collected and analyzed to identify potential issues with the system’s operation. A correlation coefficient model was used to detect anomalies accurately in the vertical carousel system to ascertain the optimal sensor placement position. This model utilized the Fisher information matrix (FIM) and effective independence (EFI) methods to optimize the sensor placement for maximum accuracy and reliability. An LSTM-autoencoder (long short-term memory) model was used for training and testing further to enhance the accuracy of the anomaly detection process. This machine-learning technique allowed for detecting patterns and trends in the vibration data that may not have been evident using traditional methods. The combination of the correlation coefficient model and the LSTM-autoencoder resulted in an accuracy rate of 97.70% for detecting anomalies in the vertical carousel system.

Unsupervised constrained discord detection in IoT-based online crane monitoring

“…Vos et al [18] [19]. In contrast, Do et al use a correlation coefficient model to determine optimal sensor placement along with an LSTM-autoencoder to detect anomalies in Vertical Carousel Storage and Retrieval Systems (VCSRS) with an accuracy of 97.70% [20].…”

LSTM-Autoencoder Deep Learning Model for Anomaly Detection in Electric Motor

“…In engineering applications, an LSTM-autoencoder model was utilized for training and testing to improve the accuracy of the anomaly detection procedure [32]. This strategy enabled the identification of patterns and trends in the vibration data that might not have been obvious when using more conventional techniques.…”

Anomaly Detection on Univariate Sensing Time Series Data for Smart Aquaculture Using Deep Learning

“…Throughout the training process, the encoder and decoder reduce input and reconstruct output via a compressed latent vector, aiming to keep network input as similar as possible to the network output. The primary distinguishing feature of an LSTM autoencoder compared to a typical autoencoder is that the primary building blocks of the network architecture are LSTM cells (Do et al 2023). The architecture of LSTM-AE is illustrated in Figure 3: Precision is calculated by dividing the number of correctly detected anomaly points by the total number of detected anomaly points.…”