Anomaly Detection in Industrial Machinery using IoT Devices and Machine Learning: a Systematic Mapping

Anomaly detection is critical in the smart industry for preventing equipment failure, reducing downtime, and improving safety. Internet of Things (IoT) has enabled the collection of large volumes of data from industrial machinery, providing a rich source of information for Anomaly Detection. However, the volume and complexity of data generated by the Internet of Things ecosystems make it difficult for humans to detect anomalies manually. Machine learning (ML) algorithms can automate anomaly detection in industrial machinery by analyzing generated data. Besides, each technique has specific strengths and weaknesses based on the data nature and its corresponding systems. However, the current systematic mapping studies on Anomaly Detection primarily focus on addressing network and cybersecurity-related problems, with limited attention given to the industrial sector. Additionally, these studies do not cover the challenges involved in using ML for Anomaly Detection in industrial machinery within the context of the IoT ecosystems. This paper presents a systematic mapping study on Anomaly Detection for industrial machinery using IoT devices and ML algorithms to address this gap. The study comprehensively evaluates 84 relevant studies spanning from 2016 to 2023, providing an extensive review of Anomaly Detection research. Our findings identify the most commonly used algorithms, preprocessing techniques, and sensor types. Additionally, this review identifies application areas and points to future challenges and research opportunities

Research on vibration-based early diagnostic system for excavator motor bearing using 1-D CNN

In mining, super-large machines such as rope excavators are used to perform the main mining operations. A rope excavator is equipped with motors that drive mechanisms. Motors are easily damaged as a result of harsh mining conditions. Bearings are important parts in a motor; bearing failure accounts for approximately half of all motor failures. Failure reduces work efficiency and increases maintenance costs. In practice, reactive, preventive, and predictive maintenance are used to minimize failures. Predictive maintenance can prevent failures and is more effective than other maintenance. For effective predictive maintenance, a good diagnosis is required to accurately determine motor-bearing health. In this study, vibration-based diagnosis and a one-dimensional convolutional neural network (1-D CNN) were used to evaluate bearing deterioration levels. The system allows for early diagnosis of bearing failures. Normal and failure-bearing vibrations were measured. Spectral and wavelet analyses were performed to determine the normal and failure vibration features. The measured signals were used to generate new data to represent bearing deterioration in increments of 10%. A reliable diagnosis system was proposed. The proposed system could determine bearing health deterioration at eleven levels with considerable accuracy. Moreover, a new data mixing method was applied

Statistical alignment in transfer learning to address the repair problem: An experimental case study

Repair is a critical step in maintenance of civil structures to ensure safe operation. However, repair can pose a problem for data-driven approaches of long-term structural health monitoring, because repairs can change the underlying distributions of the data, which can invalidate models trained on pre-repair data. As a result, models previously trained on pre-repair information fail to generalise to post-repair data, reducing their performances and misrepresenting the actual behaviour of structures. This paper suggests a population-based structural health monitoring approach to address the problem of repair in long-term monitoring of a mast structure, by exploring domain adaptation techniques developed for transfer learning. A combined approach of normal condition alignment and Dirichlet process mixture models is adopted here for damage detection, that can operate unimpeded by post-repair shifts in distributions. The method is able correctly identify 99\% of the damage data with a false positive rate of around 1.6%. Moreover, it is able to detect environmental variations such as stiffening due to freezing conditions that can adversely affect the dynamic behaviour of structures

Next-generation energy systems for sustainable smart cities: Roles of transfer learning

Smart cities attempt to reach net-zero emissions goals by reducing wasted energy while improving grid stability and meeting service demand. This is possible by adopting next-generation energy systems, which leverage artificial intelligence, the Internet of things (IoT), and communication technologies to collect and analyze big data in real-time and effectively run city services. However, training machine learning algorithms to perform various energy-related tasks in sustainable smart cities is a challenging data science task. These algorithms might not perform as expected, take much time in training, or do not have enough input data to generalize well. To that end, transfer learning (TL) has been proposed as a promising solution to alleviate these issues. To the best of the authors’ knowledge, this paper presents the first review of the applicability of TL for energy systems by adopting a well-defined taxonomy of existing TL frameworks. Next, an in-depth analysis is carried out to identify the pros and cons of current techniques and discuss unsolved issues. Moving on, two case studies illustrating the use of TL for (i) energy prediction with mobility data and (ii) load forecasting in sports facilities are presented. Lastly, the paper ends with a discussion of the future directions

IoT Data Analytics in Dynamic Environments: From An Automated Machine Learning Perspective

With the wide spread of sensors and smart devices in recent years, the data generation speed of the Internet of Things (IoT) systems has increased dramatically. In IoT systems, massive volumes of data must be processed, transformed, and analyzed on a frequent basis to enable various IoT services and functionalities. Machine Learning (ML) approaches have shown their capacity for IoT data analytics. However, applying ML models to IoT data analytics tasks still faces many difficulties and challenges, specifically, effective model selection, design/tuning, and updating, which have brought massive demand for experienced data scientists. Additionally, the dynamic nature of IoT data may introduce concept drift issues, causing model performance degradation. To reduce human efforts, Automated Machine Learning (AutoML) has become a popular field that aims to automatically select, construct, tune, and update machine learning models to achieve the best performance on specified tasks. In this paper, we conduct a review of existing methods in the model selection, tuning, and updating procedures in the area of AutoML in order to identify and summarize the optimal solutions for every step of applying ML algorithms to IoT data analytics. To justify our findings and help industrial users and researchers better implement AutoML approaches, a case study of applying AutoML to IoT anomaly detection problems is conducted in this work. Lastly, we discuss and classify the challenges and research directions for this domain.Comment: Published in Engineering Applications of Artificial Intelligence (Elsevier, IF:7.8); Code/An AutoML tutorial is available at Github link: https://github.com/Western-OC2-Lab/AutoML-Implementation-for-Static-and-Dynamic-Data-Analytic