2,259 research outputs found
Distributed Anomaly Detection using Autoencoder Neural Networks in WSN for IoT
Wireless sensor networks (WSN) are fundamental to the Internet of Things
(IoT) by bridging the gap between the physical and the cyber worlds. Anomaly
detection is a critical task in this context as it is responsible for
identifying various events of interests such as equipment faults and
undiscovered phenomena. However, this task is challenging because of the
elusive nature of anomalies and the volatility of the ambient environments. In
a resource-scarce setting like WSN, this challenge is further elevated and
weakens the suitability of many existing solutions. In this paper, for the
first time, we introduce autoencoder neural networks into WSN to solve the
anomaly detection problem. We design a two-part algorithm that resides on
sensors and the IoT cloud respectively, such that (i) anomalies can be detected
at sensors in a fully distributed manner without the need for communicating
with any other sensors or the cloud, and (ii) the relatively more
computation-intensive learning task can be handled by the cloud with a much
lower (and configurable) frequency. In addition to the minimal communication
overhead, the computational load on sensors is also very low (of polynomial
complexity) and readily affordable by most COTS sensors. Using a real WSN
indoor testbed and sensor data collected over 4 consecutive months, we
demonstrate via experiments that our proposed autoencoder-based anomaly
detection mechanism achieves high detection accuracy and low false alarm rate.
It is also able to adapt to unforeseeable and new changes in a non-stationary
environment, thanks to the unsupervised learning feature of our chosen
autoencoder neural networks.Comment: 6 pages, 7 figures, IEEE ICC 201
Intelligent Condition Monitoring of Industrial Plants: An Overview of Methodologies and Uncertainty Management Strategies
Condition monitoring plays a significant role in the safety and reliability
of modern industrial systems. Artificial intelligence (AI) approaches are
gaining attention from academia and industry as a growing subject in industrial
applications and as a powerful way of identifying faults. This paper provides
an overview of intelligent condition monitoring and fault detection and
diagnosis methods for industrial plants with a focus on the open-source
benchmark Tennessee Eastman Process (TEP). In this survey, the most popular and
state-of-the-art deep learning (DL) and machine learning (ML) algorithms for
industrial plant condition monitoring, fault detection, and diagnosis are
summarized and the advantages and disadvantages of each algorithm are studied.
Challenges like imbalanced data, unlabelled samples and how deep learning
models can handle them are also covered. Finally, a comparison of the
accuracies and specifications of different algorithms utilizing the Tennessee
Eastman Process (TEP) is conducted. This research will be beneficial for both
researchers who are new to the field and experts, as it covers the literature
on condition monitoring and state-of-the-art methods alongside the challenges
and possible solutions to them
Fault Detection and Diagnosis of Electric Drives Using Intelligent Machine Learning Approaches
Electric motor condition monitoring can detect anomalies in the motor performance which have the potential to result in unexpected failure and financial loss. This study examines different fault detection and diagnosis approaches in induction motors and is presented in six chapters. First, an anomaly technique or outlier detection is applied to increase the accuracy of detecting broken rotor bars. It is shown how the proposed method can significantly improve network reliability by using one-class classification technique. Then, ensemble-based anomaly detection is utilized to compare different methods in ensemble learning in detection of broken rotor bars. Finally, a deep neural network is developed to extract significant features to be used as input parameters of the network. Deep autoencoder is then employed to build an advanced model to make predictions of broken rotor bars and bearing faults occurring in induction motors with a high accuracy
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