Data-driven Machinery Fault Detection: A Comprehensive Review

In this era of advanced manufacturing, it's now more crucial than ever to diagnose machine faults as early as possible to guarantee their safe and efficient operation. With the massive surge in industrial big data and advancement in sensing and computational technologies, data-driven Machinery Fault Diagnosis (MFD) solutions based on machine/deep learning approaches have been used ubiquitously in manufacturing. Timely and accurately identifying faulty machine signals is vital in industrial applications for which many relevant solutions have been proposed and are reviewed in many articles. Despite the availability of numerous solutions and reviews on MFD, existing works often lack several aspects. Most of the available literature has limited applicability in a wide range of manufacturing settings due to their concentration on a particular type of equipment or method of analysis. Additionally, discussions regarding the challenges associated with implementing data-driven approaches, such as dealing with noisy data, selecting appropriate features, and adapting models to accommodate new or unforeseen faults, are often superficial or completely overlooked. Thus, this survey provides a comprehensive review of the articles using different types of machine learning approaches for the detection and diagnosis of various types of machinery faults, highlights their strengths and limitations, provides a review of the methods used for condition-based analyses, comprehensively discusses the available machinery fault datasets, introduces future researchers to the possible challenges they have to encounter while using these approaches for MFD and recommends the probable solutions to mitigate those problems. The future research prospects are also pointed out for a better understanding of the field. We believe this article will help researchers and contribute to the further development of the field.

HUMS2023 Data Challenge Result Submission

We implemented a simple method for early detection in this research. The implemented methods are plotting the given mat files and analyzing scalogram images generated by performing Continuous Wavelet Transform (CWT) on the samples. Also, finding the mean, standard deviation (STD), and peak-to-peak (P2P) values from each signal also helped detect faulty signs. We have implemented the autoregressive integrated moving average (ARIMA) method to track the progression.

SHINE: Deep Learning-Based Accessible Parking Management System

The enhancement of science and technology has helped expand urban cities like never before. Due to the undeniable benefits of owning a private vehicle, the number of cars has rocketed in many parts of the world, including South Korea. However, these gradual increments in the number of vehicles lead to parking-related problems, including the abuse of disabled parking spaces (referred to as accessible parking spaces hereafter). Due to the high frame rate of surveillance cameras, traditional license plate recognition (LPR) systems are ineffective in real-time. On the other hand, natural and artificial noise and differences in lighting and weather conditions make detection and recognition difficult for these systems. With the growing concept of parking 4.0, many sensors, IoT and deep learning-based approaches have been applied to automatic LPR and parking management systems. However, the studies show a need for a robust and efficient model for managing accessible parking spaces in South Korea. We have proposed a novel system called 'SHINE', which uses the deep learning-based object detection algorithm for detecting the vehicle, license plate, and disability badges (referred to as cards, badges, or access badges hereafter) and then authenticates the rights to use the accessible parking spaces by coordinating with the central server. This model, achieving 92.16% mean average precision, is believed to solve the problem of accessible parking space abuse.