An anomaly detection method based on random convolutional kernel and isolation forest for equipment state monitoring

Abstract

Anomaly detection plays an essential role in health monitoring and reliability assurance of complex system. However, previous researches suffer from distraction by outliers in training and extensively relying on empiric-based feature engineering, leading to many limitations in the practical application of detection methods. In this paper, we propose an unsupervised anomaly detection method that combines random convolution kernels with isolation forest to tackle the above problems in equipment state monitoring. The random convolution kernels are applied to generate cross-dimensional and multi-scale features for multi-dimensional time series, with combining the time series decomposing method to select abnormally sensitive features for automatic feature extraction. Then, anomaly detection is performed on the obtained features using isolation forests with low requirements for purity of training sample. The verification and comparison on different types of datasets show the performance of the proposed method surpass the traditional methods in accuracy and applicability.

Detection and Determination of User Position Using Radio Tomography with Optimal Energy Consumption of Measuring Devices in Smart Buildings

“…Neural networks (NNs) [4,8,9,21,23,24,29] are one of the most used types of machine learning methods and are usually utilized for classification or regression analysis. A NN is created by developing multi-layer perceptions in a particular order.…”

“…Both platforms represent the ARM architecture and Cortex cores. Support for the IEEE 802.15.4 communication module can also be used interchangeably between these two units [20,21].…”

Optimizing the Neural Network Loss Function in Electrical Tomography to Increase Energy Efficiency in Industrial Reactors

“…This evolution, as pointed out by Reis and Gins, reflects the increasing complexity and sophistication of monitoring techniques that have become indispensable to modern industrial operations [5]. Anomaly detection, as a core technology, has played a key role in ensuring the safety and reliability of the system [6]. In recent years, preventive maintenance, especially for machinery, has become increasingly important as a strategic approach to optimizing operations and ensuring longevity [7].…”

Dimensionality reduction of rotor fault dataset based on joint embedding of multi-class graphs

“…In engineering applications, it is usually necessary to install multiple sensors at the key cross-section of the system to collect multi-channel information and extract statistical features from the vibration signals collected from each channel, but the increase in the number of features will undoubtedly produce the problem of "dimensional catastrophe" [27,19,15,14,10]. crucial for developing machine intelligence fault diagnosis and decision-making techniques for industrial big data [23,2,20].…”