IoT-Based Vibration Sensor Data Collection and Emergency Detection Classification using Long Short Term Memory (LSTM)

Nwakanma, Cosmas Ifeanyi; Islam, Fabliha Bushra; Maharani, Mareska Pratiwi; Kim, Dong-Seong; Lee, Jae-Min

doi:10.1109/icaiic51459.2021.9415228

Cited by 17 publications

(12 citation statements)

References 18 publications

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“…Deep learning models have been very successful in fault prediction and predictive preventive maintenance (Guo et al, 2021). Zhang et al In the case of vibration analysis, LSTM networks can analyse the internal correlation of vibration signals among time series data (An et al, 2020;Guo et al, 2021;Huang et al, 2021;Nwakanma et al, 2021;Zhao et al, 2017;Zheng et al, 2017). Zhao et al (2017) proposed using a Convolutional Bi-directional LSTM for fault prediction.…”

Section: Fault Diagnosis Methodsmentioning

confidence: 99%

Explainable fault prediction using learning fuzzy cognitive maps

Mansouri

Vadera

2023

Expert Systems

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IoT sensors capture different aspects of the environment and generate high throughput data streams. Besides capturing these data streams and reporting the monitoring information, there is significant potential for adopting deep learning to identify valuable insights for predictive preventive maintenance. One specific class of applications involves using Long Short‐Term Memory Networks (LSTMs) to predict faults happening in the near future. However, despite their remarkable performance, LSTMs can be very opaque. This paper deals with this issue by applying Learning Fuzzy Cognitive Maps (LFCMs) for developing simplified auxiliary models that can provide greater transparency. An LSTM model for predicting faults of industrial bearings based on readings from vibration sensors is developed to evaluate the idea. An LFCM is then used to imitate the performance of the baseline LSTM model. Through static and dynamic analyses, we demonstrate that LFCM can highlight (i) which members in a sequence of readings contribute to the prediction result and (ii) which values could be controlled to prevent possible faults. Moreover, we compare LFCM with state‐of‐the‐art methods reported in the literature, including decision trees and SHAP values. The experiments show that LFCM offers some advantages over these methods. Moreover, LFCM, by conducting a what‐if analysis, could provide more information about the black‐box model. To the best of our knowledge, this is the first time LFCMs have been used to simplify a deep learning model to offer greater explainability.

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Section: Fault Diagnosis Methodsmentioning

confidence: 99%

Explainable fault prediction using learning fuzzy cognitive maps

Mansouri

Vadera

2023

Expert Systems

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“…However, the result of Reference [35] gave an accuracy of 96%. Similarly, authors in Reference [36] proposed a long short term memory (LSTM) model to detect and classify abnormal vibrations for emergency detection. However, CNN has been shown to perform better for human action recognition data and most suitable to such classes/type of data [37].…”

Section: Machine Learning and Neural Network Approaches To Smart Fact...mentioning

confidence: 99%

Detection and Classification of Human Activity for Emergency Response in Smart Factory Shop Floor

et al. 2021

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Factory shop floor workers are exposed to threats and accidents due to their encounters with tools, equipment, and toxic materials. There are cases of occupational accidents resulting in injuries to workers and precipitating lawsuits, which on the other hand affect company’s operational cost. To ensure the safety of workers within the shop floor, there is a need for proactive activity monitoring. Such activities include detection of falling objects, abnormal vibration, and movement of humans within an acceptable area of the factory floor. Breathing sensor-based monitoring of workers in the smart factory shop floor can also be implemented. This is for the detection of human activity, especially in cases where workers are in isolation with no available emergency assistance. Internet of Things (IoT), Industrial Internet of Things (IIoT), and machine learning (ML) have enabled so many possibilities in this area. In this study, we present a simple test-bed, which is made up of a vibration sensor, a breathing and movement sensor, and a Light Detection and Ranging (LIDAR) sensor. These sensors were used to gather normal and abnormal data of human activities at the factory. We developed a dataset based on possible real-life situations and it is made up of about 10,000 data points. The data was split with a ratio of 75:25 for training and testing the model. We investigated the performance of different ML algorithms, including support vector machine (SVM), linear regression, naive Bayes (NB), K-nearest neighbor (KNN), and convolutional neural network (CNN). From our experiments, the CNN model outperformed other algorithms with an accuracy of 99.45%, 99.78%,100%, and 100%, respectively, for vibration, movement, breathing, and distance. We have also successfully developed a dataset to assist the research community in this field.

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“…N OWADAYS, in light of emerging technologies such as 5G, cloud computing, and fast-growing sensor manufacturing technologies, the Internet of Things is getting more popular. Among a wide variety of applications, condition monitoring is a demanding IoT application [1], [2]. In this use case, different sensors are installed on equipment to monitor various aspects to identify their condition or conduct analytical methods to predict a fault in the future [3], [4].…”

Section: Introductionmentioning

confidence: 99%

“…For vibration analysis, LSTM analyzes the inherent correlation of vibration signals in the processing of time series data, so it is increasingly adopted in data-driven fault prediction [1], [3], [12], [27], [28]. [12] proposed a Convolutional Bi-directional LSTM to predict machinery faults using sensor data.…”

Section: Introductionmentioning

confidence: 99%

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A Deep Explainable Model for Fault Prediction Using IoT Sensors

Mansouri

Vadera

2022

IEEE Access

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IoT sensors and deep learning models can widely be applied for fault prediction. Although deep learning models are considerably more potent than many conventional machine learning models, they are not transparent. This paper first examines different deep learning techniques to carry out univariate time series analysis based on vibration sensors installed on four industrial bearings to predict a fault occurring in a predefined time window. Several recurrent neural networks are used to develop fault prediction models. An empirical evaluation of these models shows that all models perform well; however, hybrid models outperform other models when the time window increases. Then, instance-wise feature selection has been considered to highlight the most contributing features for its outputs regarding any input. In this problem, the main challenge is to propose a trainable feature selection model with the minimum number of selected features whilst its performance is close to the baseline model. This paper develops a novel explainable method called the Gumbel-Sigmoid eXplanator (GSX) to tackle these problems. In a nutshell: (i) we have developed a differentiable and trainable selector, and (ii) we utilize regularization to control the number of features for each instance flexibly. The proposed method is model agnostic, and empirical evaluations on two datasets show that GSX can not only solve the problems identified with two other state-of-the-art methods but also outperform them in terms of accuracy and run-time.

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IoT-Based Vibration Sensor Data Collection and Emergency Detection Classification using Long Short Term Memory (LSTM)

Cited by 17 publications

References 18 publications

Explainable fault prediction using learning fuzzy cognitive maps

Explainable fault prediction using learning fuzzy cognitive maps

Detection and Classification of Human Activity for Emergency Response in Smart Factory Shop Floor

A Deep Explainable Model for Fault Prediction Using IoT Sensors

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