scholarly journals A Time-Distributed Spatiotemporal Feature Learning Method for Machine Health Monitoring with Multi-Sensor Time Series

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 2932 ◽  
Author(s):  
Huihui Qiao ◽  
Taiyong Wang ◽  
Peng Wang ◽  
Shibin Qiao ◽  
Lan Zhang
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Health Monitoring ◽  
Data Driven ◽  
Sensor Data ◽  
Series Data ◽  
Spatiotemporal Features ◽  
Daunting Task ◽  
Machine Health Monitoring ◽  
Machine Health

Data-driven methods with multi-sensor time series data are the most promising approaches for monitoring machine health. Extracting fault-sensitive features from multi-sensor time series is a daunting task for both traditional data-driven methods and current deep learning models. A novel hybrid end-to-end deep learning framework named Time-distributed ConvLSTM model (TDConvLSTM) is proposed in the paper for machine health monitoring, which works directly on raw multi-sensor time series. In TDConvLSTM, the normalized multi-sensor data is first segmented into a collection of subsequences by a sliding window along the temporal dimension. Time-distributed local feature extractors are simultaneously applied to each subsequence to extract local spatiotemporal features. Then a holistic ConvLSTM layer is designed to extract holistic spatiotemporal features between subsequences. At last, a fully-connected layer and a supervised learning layer are stacked on the top of the model to obtain the target. TDConvLSTM can extract spatiotemporal features on different time scales without any handcrafted feature engineering. The proposed model can achieve better performance in both time series classification tasks and regression prediction tasks than some state-of-the-art models, which has been verified in the gearbox fault diagnosis experiment and the tool wear prediction experiment.

scholarly journals Remaining Useful Life Estimation from Event Data

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Mahbubul Alam ◽  
Laleh Jalali ◽  
Mahbubul Alam ◽  
Ahmed Farahat ◽  
Chetan Gupta
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Time Series Data ◽  
Remaining Useful Life ◽  
Sensor Data ◽  
Series Data ◽  
Sequential Data ◽  
Event Data ◽  
Useful Life ◽  
Event Based

Abstract—Prognostics aims to predict the degradation of equipment by estimating their remaining useful life (RUL) and/or the failure probability within a specific time horizon. The high demand of equipment prognostics in the industry have propelled researchers to develop robust and efficient prognostics techniques. Among data driven techniques for prognostics, machine learning and deep learning (DL) based techniques, particularly Recurrent Neural Networks (RNNs) have gained significant attention due to their ability of effectively representing the degradation progress by employing dynamic temporal behaviors. RNNs are well known for handling sequential data, especially continuous time series sequential data where the data follows certain pattern. Such data is usually obtained from sensors attached to the equipment. However, in many scenarios sensor data is not readily available and often very tedious to acquire. Conversely, event data is more common and can easily be obtained from the error logs saved by the equipment and transmitted to a backend for further processing. Nevertheless, performing prognostics using event data is substantially more difficult than that of the sensor data due to the unique nature of event data. Though event data is sequential, it differs from other seminal sequential data such as time series and natural language in the following manner, i) unlike time series data, events may appear at any time, i.e., the appearance of events lacks periodicity; ii) unlike natural languages, event data do not follow any specific linguistic rule. Additionally, there may be a significant variability in the event types appearing within the same sequence.  Therefore, this paper proposes an RUL estimation framework to effectively handle the intricate and novel event data. The proposed framework takes discrete events generated by an equipment (e.g., type, time, etc.) as input, and generates for each new event an estimate of the remaining operating cycles in the life of a given component. To evaluate the efficacy of our proposed method, we conduct extensive experiments using benchmark datasets such as the CMAPSS data after converting the time-series data in these datasets to sequential event data. The event data conversion is carried out by careful exploration and application of appropriate transformation techniques to the time series. To the best of our knowledge this is the first time such event-based RUL estimation problem is introduced to the community. Furthermore, we propose several deep learning and machine learning based solution for the event-based RUL estimation problem. Our results suggest that the deep learning models, 1D-CNN, LSTM, and multi-head attention show similar RMSE, MAE and Score performance. Foreseeably, the XGBoost model achieve lower performance compared to the deep learning models since the XGBoost model fails to capture ordering information from the sequence of events. 

scholarly journals Deep Learning for Infrared Thermal Image Based Machine Health Monitoring

2018 ◽  
Vol 23 (1) ◽  
pp. 151-159 ◽  
Author(s):  
Olivier Janssens ◽  
Rik Van de Walle ◽  
Mia Loccufier ◽  
Sofie Van Hoecke
Keyword(s):  
Deep Learning ◽  
Health Monitoring ◽  
Thermal Image ◽  
Machine Health Monitoring ◽  
Machine Health

scholarly journals A Review on Anomaly Detection in Time Series

2021 ◽  
Vol 10 (3) ◽  
pp. 1895-1900
Keyword(s):  
Time Series ◽  
Anomaly Detection ◽  
Time Series Data ◽  
Monitoring Network ◽  
Series Data ◽  
Data Sets ◽  
Data Set ◽  
The Past ◽  
Machine Health Monitoring ◽  
Machine Health

Time series is a very common class of data sets. Among others, it is very simple to obtain time series data from a variety of various science and finance applications and an anomaly detection technique for time series is becoming a very prominent research topic nowadays. Anomaly identification covers intrusion detection, detection of theft, mistake detection, machine health monitoring, network sensor event detection or habitat disturbance. It is also used for removing suspicious data from the data set before production. This review aims to provide a detailed and organized overview of the Anomaly detection investigation. In this article we will first define what an anomaly in time series is, and then describe quickly some of the methods suggested in the past two or three years for detection of anomaly in time series

Deep learning and its applications to machine health monitoring

2019 ◽  
Vol 115 ◽  
pp. 213-237 ◽  
Author(s):  
Rui Zhao ◽  
Ruqiang Yan ◽  
Zhenghua Chen ◽  
Kezhi Mao ◽  
Peng Wang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Health Monitoring ◽  
Machine Health Monitoring ◽  
Machine Health

A new dam structural response estimation paradigm powered by deep learning and transfer learning techniques

2021 ◽  
pp. 147592172110097
Author(s):  
Yangtao Li ◽  
Tengfei Bao ◽  
Zhixin Gao ◽  
Xiaosong Shu ◽  
Kang Zhang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Deep Learning ◽  
Transfer Learning ◽  
Health Monitoring ◽  
Structural Response ◽  
Data Driven ◽  
Structural Health ◽  
Learning Techniques

With the rapid development of information and communication techniques, dam structural health assessment based on data collected from structural health monitoring systems has become a trend. This allows for applying data-driven methods for dam safety analysis. However, data-driven models in most related literature are statistical and shallow machine learning models, which cannot capture the time series patterns or learn from long-term dependencies of dam structural response time series. Furthermore, the effectiveness and applicability of these models are only validated in a small data set and part of monitoring points in a dam structural health monitoring system. To address the problems, this article proposes a new modeling paradigm based on various deep learning and transfer learning techniques. The paradigm utilizes one-dimensional convolutional neural networks to extract the inherent features from dam structural response–related environmental quantity monitoring data. Then bidirectional gated recurrent unit with a self-attention mechanism is used to learn from long-term dependencies, and transfer learning is utilized to transfer knowledge learned from the typical monitoring point to the others. The proposed paradigm integrates the powerful modeling capability of deep learning networks and the flexible transferability of transfer learning. Rather than traditional models that rely on experience for feature selection, the proposed deep learning–based paradigm directly utilizes environmental monitoring time series as inputs to accurately estimate dam structural response changes. A high arch dam in long-term service is selected as the case study, and three monitoring items, including dam displacement, crack opening displacement, and seepage are used as the research objects. The experimental results show that the proposed paradigm outperforms conventional and shallow machine learning–based methods in all 41 tested monitoring points, which indicates that the proposed paradigm is capable of dealing with dam structural response estimation with high accuracy and robustness.

scholarly journals IIoT Machine Health Monitoring Models for Education and Training

2021 ◽  
Author(s):  
Ishwar Singh ◽  
Sean Hodgins ◽  
Anoop Gadhrri ◽  
Reiner Schmidt
Keyword(s):  
Health Monitoring ◽  
Education And Training ◽  
Predictive Maintenance ◽  
Sensor Data ◽  
Sensor Technology ◽  
Transportation Energy ◽  
Machine Health Monitoring ◽  
Machine Health ◽  
Industry Sectors ◽  
And Training

IoT, IIoT and Industry 4.0 technologies are leading the way for digital transformation in manufacturing, healthcare, transportation, energy, retail, cities, supply chain, agriculture, buildings, and other sectors. Machine health monitoring and predictive maintenance of rotating machines is an innovative IIoT use case in the manufacturing and energy sectors. This chapter covers how machine health monitoring can be implemented using advanced sensor technology as a basis for predictive maintenance in rotating devices. It also covers how sensor data can be collected from the devices at the edge, preprocessed in a microcontroller/edge node, and sent to the cloud or local server for advanced data intelligence. In addition, this chapter describes the design and operation of three innovative models for education and training supporting the accelerated adoption of these technologies in industry sectors.

scholarly journals Load forecasting of refrigerated display cabinet based on CEEMD–IPSO–LSTM combined model

Open Physics ◽  
2021 ◽  
Vol 19 (1) ◽  
pp. 360-374
Author(s):  
Yuan Pei ◽  
Lei Zhenglin ◽  
Zeng Qinghui ◽  
Wu Yixiao ◽  
Lu Yanli ◽  
...  
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Time Series Data ◽  
Load Forecasting ◽  
Series Data ◽  
Forecasting Model ◽  
Combined Model ◽  
Forecasting Accuracy ◽  
Forecasting Method ◽  
Consumption Reduction

Abstract The load of the showcase is a nonlinear and unstable time series data, and the traditional forecasting method is not applicable. Deep learning algorithms are introduced to predict the load of the showcase. Based on the CEEMD–IPSO–LSTM combination algorithm, this paper builds a refrigerated display cabinet load forecasting model. Compared with the forecast results of other models, it finally proves that the CEEMD–IPSO–LSTM model has the highest load forecasting accuracy, and the model’s determination coefficient is 0.9105, which is obviously excellent. Compared with other models, the model constructed in this paper can predict the load of showcases, which can provide a reference for energy saving and consumption reduction of display cabinet.

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