Detecting Production Phases Based on Sensor Values using 1D-CNNs

In the context of Industry 4.0, the knowledge extraction from sensor information plays an important role. Often, information gathered from sensor values reveals meaningful insights for production levels, such as anomalies or machine states. In our use case, we identify production phases through the inspection of sensor values with the help of convolutional neural networks. The data set stems from a tempering furnace used for metal heat treating. Our supervised learning approach unveils a promising accuracy for the chosen neural network that was used for the detection of production phases. We consider solutions like shown in this work as salient pillars in the field of predictive maintenance

Intelligent fault detection and classification based on hybrid deep learning methods for Hardware-in-the-Loop test of automotive software systems

Hardware-in-the-Loop (HIL) has been recommended by ISO 26262 as an essential test bench for determining the safety and reliability characteristics of automotive software systems (ASSs). However, due to the complexity and the huge amount of data recorded by the HIL platform during the testing process, the conventional data analysis methods used for detecting and classifying faults based on the human expert are not realizable. Therefore, the development of effective means based on the historical data set is required to analyze the records of the testing process in an efficient manner. Even though data-driven fault diagnosis is superior to other approaches, selecting the appropriate technique from the wide range of Deep Learning (DL) techniques is challenging. Moreover, the training data containing the automotive faults are rare and considered highly confidential by the automotive industry. Using hybrid DL techniques, this study proposes a novel intelligent fault detection and classification (FDC) model to be utilized during the V-cycle development process, i.e., the system integration testing phase. To this end, an HIL-based real-time fault injection framework is used to generate faulty data without altering the original system model. In addition, a combination of the Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) is employed to build the model structure. In this study, eight types of sensor faults are considered to cover the most common potential faults in the signals of ASSs. As a case study, a gasoline engine system model is used to demonstrate the capabilities and advantages of the proposed method and to verify the performance of the model. The results prove that the proposed method shows better detection and classification performance compared to other standalone DL methods. Specifically, the overall detection accuracies of the proposed structure in terms of precision, recall and F1-score are 98.86%, 98.90% and 98.88%, respectively. For classification, the experimental results also demonstrate the superiority under unseen test data with an average accuracy of 98.8%

Machine learning and deep learning based methods toward Industry 4.0 predictive maintenance in induction motors: Α state of the art survey

Purpose: Developments in Industry 4.0 technologies and Artificial Intelligence (AI) have enabled data-driven manufacturing. Predictive maintenance (PdM) has therefore become the prominent approach for fault detection and diagnosis (FD/D) of induction motors (IMs). The maintenance and early FD/D of IMs are critical processes, considering that they constitute the main power source in the industrial production environment. Machine learning (ML) methods have enhanced the performance and reliability of PdM. Various deep learning (DL) based FD/D methods have emerged in recent years, providing automatic feature engineering and learning and thereby alleviating drawbacks of traditional ML based methods. This paper presents a comprehensive survey of ML and DL based FD/D methods of IMs that have emerged since 2015. An overview of the main DL architectures used for this purpose is also presented. A discussion of the recent trends is given as well as future directions for research. Design/methodology/approach: A comprehensive survey has been carried out through all available publication databases using related keywords. Classification of the reviewed works has been done according to the main ML and DL techniques and algorithms Findings: DL based PdM methods have been mainly introduced and implemented for IM fault diagnosis in recent years. Novel DL FD/D methods are based on single DL techniques as well as hybrid techniques. DL methods have also been used for signal preprocessing and moreover, have been combined with traditional ML algorithms to enhance the FD/D performance in feature engineering. Publicly available datasets have been mostly used to test the performance of the developed methods, however industrial datasets should become available as well. Multi-agent system (MAS) based PdM employing ML classifiers has been explored. Several methods have investigated multiple IM faults, however, the presence of multiple faults occurring simultaneously has rarely been investigated. Originality/value: The paper presents a comprehensive review of the recent advances in PdM of IMs based on ML and DL methods that have emerged since 2015Peer Reviewe

Face Emotion Recognition Based on Machine Learning: A Review

Computers can now detect, understand, and evaluate emotions thanks to recent developments in machine learning and information fusion. Researchers across various sectors are increasingly intrigued by emotion identification, utilizing facial expressions, words, body language, and posture as means of discerning an individual's emotions. Nevertheless, the effectiveness of the first three methods may be limited, as individuals can consciously or unconsciously suppress their true feelings. This article explores various feature extraction techniques, encompassing the development of machine learning classifiers like k-nearest neighbour, naive Bayesian, support vector machine, and random forest, in accordance with the established standard for emotion recognition. The paper has three primary objectives: firstly, to offer a comprehensive overview of effective computing by outlining essential theoretical concepts; secondly, to describe in detail the state-of-the-art in emotion recognition at the moment; and thirdly, to highlight important findings and conclusions from the literature, with an emphasis on important obstacles and possible future paths, especially in the creation of state-of-the-art machine learning algorithms for the identification of emotions

CASPER: Context-Aware IoT Anomaly Detection System for Industrial Robotic Arms

Industrial cyber-physical systems (ICPS) are widely employed in supervising and controlling critical infrastructures (CIs), with manufacturing systems that incorporate industrial robotic arms being a prominent example. The increasing adoption of ubiquitous computing technologies in these systems has led to benefits such as real-time monitoring, reduced maintenance costs, and high interconnectivity. This adoption has also brought cybersecurity vulnerabilities exploited by adversaries disrupting manufacturing processes via manipulating actuator behaviors. Previous incidents in the industrial cyber domain prove that adversaries launch sophisticated attacks rendering network-based anomaly detection mechanisms insufficient as the "physics" involved in the process is overlooked. To address this issue, we propose an IoT-based cyber-physical anomaly detection system that can detect motion-based behavioral changes in an industrial robotic arm. We apply both statistical and state-of-the-art machine learning (ML) methods to real-time Inertial Measurement Unit (IMU) data collected from an edge development board attached to an arm doing a pick-and-place operation. To generate anomalies, we modify the joint velocity of the arm. Our goal is to create an air-gapped secondary protection layer to detect "physical" anomalies without depending on the integrity of network data, thus augmenting overall anomaly detection capability. Our empirical results show that the proposed system, which utilizes 1D-CNNs, can successfully detect motion-based anomalies on a real-world industrial robotic arm. The significance of our work lies in its contribution to developing a comprehensive solution for ICPS security, which goes beyond conventional network-based methods

Big data architecture for intelligent maintenance : a focus on query processing and machine learning algorithms

Exploiting available condition monitoring data of industrial machines for intelligent maintenance purposes has been attracting attention in various application fields. Machine learning algorithms for fault detection, diagnosis and prognosis are popular and easily accessible. However, our experience in working at the intersection of academia and industry showed that the major challenges of building an end-to-end system in a real-world industrial setting go beyond the design of machine learning algorithms. One of the major challenges is the design of an end-to-end data management solution that is able to efficiently store and process large amounts of heterogeneous data streams resulting from a variety of physical machines. In this paper we present the design of an end-to-end Big Data architecture that enables intelligent maintenance in a real-world industrial setting. In particular, we will discuss various physical design choices for optimizing high-dimensional queries, such as partitioning and Z-ordering, that serve as the basis for health analytics. Finally, we describe a concrete fault detection use case with two different health monitoring algorithms based on machine learning and classical statistics and discuss their advantages and disadvantages. The paper covers some of the most important aspects of the practical implementation of such an end-to-end solution and demonstrates the challenges and their mitigation for the specific application of laser cutting machines