Enhancing Multivariate Time Series Classification Using LSTM and Evidence Feed Forward HMM

Tripathi, Achyut Mani

doi:10.1109/ijcnn48605.2020.9207636

Cited by 1 publication

(1 citation statement)

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“…In addition to CNN, the Evidence Feed Forward Hidden Markov Model (EFF-HMM) has been combined with LSTM to classify MTS. According to [61], learning EFF-HMM is based on the mistakes of the LSTM that outperformed other state of the art in human activity recognition.…”

Section: Deep Learning Algorithmsmentioning

confidence: 99%

Multivariate Time-Series Classification of Critical Events from Industrial Drying Hopper Operations: A Deep Learning Approach

Rahman,

Farahani,

Wuest

2023

JMMP

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In recent years, the advancement of Industry 4.0 and smart manufacturing has made a large amount of industrial process data attainable with the use of sensors installed on machines. This paper proposes an experimental predictive maintenance framework for an industrial drying hopper so that it can detect any unusual event in the hopper, which reduces the risk of erroneous fault diagnosis in the manufacturing shop floor. The experimental framework uses Deep Learning (DL) algorithms to classify Multivariate Time-Series (MTS) data into two categories—failure or unusual events and regular events—thus formulating the problem as a binary classification. The raw data extracted from the sensors contained missing values, suffered from imbalancedness, and were not labeled. Therefore, necessary preprocessing is performed to make them usable for DL algorithms and the dataset is self-labeled after defining the two categories precisely. To tackle the imbalanced data issue, data balancing techniques like ensemble learning with undersampling and Synthetic Minority Oversampling Technique (SMOTE) are used. Moreover, along with DL algorithms like Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM), Machine Learning (ML) algorithms like Support Vector Machine (SVM) and K-nearest neighbor (KNN) have also been used to perform a comparative analysis on the results obtained from these algorithms. The result shows that CNN is arguably the best algorithm for classifying this dataset into two categories and outperforms other traditional approaches as well as deep learning algorithms.

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Section: Deep Learning Algorithmsmentioning

confidence: 99%