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 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.

Adaptive Weighted Signal Preprocessing Technique for Machine Health Monitoring

2021 ◽  
Vol 70 ◽  
pp. 1-11
Author(s):  
Bingchang Hou ◽  
Dong Wang ◽  
Yi Wang ◽  
Tongtong Yan ◽  
Zhike Peng ◽  
...  
Keyword(s):  
Health Monitoring ◽  
Signal Preprocessing ◽  
Machine Health Monitoring ◽  
Machine Health ◽  
Preprocessing Technique

Machine Health Monitoring of Induction Motors

2019 ◽  
pp. 51-56
Author(s):  
Panchaksharayya S. Hiremath ◽  
Kalyan Ram B. ◽  
Santoshgouda M. Patil ◽  
V. Sabarish ◽  
Preeti Biradar ◽  
...  
Keyword(s):  
Health Monitoring ◽  
Induction Motors ◽  
Machine Health Monitoring ◽  
Machine Health

scholarly journals Identification of Bridge Key Performance Indicators Using Survival Analysis for Future Network-Wide Structural Health Monitoring

Sensors ◽  
2020 ◽  
Vol 20 (23) ◽  
pp. 6894
Author(s):  
Nicola-Ann Stevens ◽  
Myra Lydon ◽  
Adele H. Marshall ◽  
Su Taylor
Keyword(s):  
Survival Analysis ◽  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Performance Indicators ◽  
Key Performance Indicators ◽  
Predictive Maintenance ◽  
Sensor Data ◽  
Transport Networks ◽  
Data Set ◽  
Structural Health

Machine learning and statistical approaches have transformed the management of infrastructure systems such as water, energy and modern transport networks. Artificial Intelligence-based solutions allow asset owners to predict future performance and optimize maintenance routines through the use of historic performance and real-time sensor data. The industrial adoption of such methods has been limited in the management of bridges within aging transport networks. Predictive maintenance at bridge network level is particularly complex due to the considerable level of heterogeneity encompassed across various bridge types and functions. This paper reviews some of the main approaches in bridge predictive maintenance modeling and outlines the challenges in their adaptation to the future network-wide management of bridges. Survival analysis techniques have been successfully applied to predict outcomes from a homogenous data set, such as bridge deck condition. This paper considers the complexities of European road networks in terms of bridge type, function and age to present a novel application of survival analysis based on sparse data obtained from visual inspections. This research is focused on analyzing existing inspection information to establish data foundations, which will pave the way for big data utilization, and inform on key performance indicators for future network-wide structural health monitoring.

Machine Health Monitoring Using Local Feature-Based Gated Recurrent Unit Networks

2018 ◽  
Vol 65 (2) ◽  
pp. 1539-1548 ◽  
Author(s):  
Rui Zhao ◽  
Dongzhe Wang ◽  
Ruqiang Yan ◽  
Kezhi Mao ◽  
Fei Shen ◽  
...  
Keyword(s):  
Health Monitoring ◽  
Local Feature ◽  
Feature Based ◽  
Machine Health Monitoring ◽  
Machine Health ◽  
Gated Recurrent Unit
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