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Artificial Intelligence Assisted Diagnosis Techniques in Smart Manufacturing

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Fault Diagnosis & Sensors".

Deadline for manuscript submissions: closed (25 June 2023) | Viewed by 25616

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Special Issue Editors

Prof. Dr. Long Wen

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Guest Editor

School of Mechanical Engineering and Electronic Information, China University of Geosciences, Wuhan 430074, China
Interests: deep learning; automatic machine learning; fault diagnosis; intelligent algorithm
Special Issues, Collections and Topics in MDPI journals

Dr. Haidong Shao

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Guest Editor

College of Mechanical and Vehicle Engineering, Hunan University, Changsha 410082, China
Interests: intelligent fault diagnosis
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Xinyu Li

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Guest Editor

School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, China
Interests: intelligent manufacturing; deep learning; machine learning; fault diagnosis; surface defect recognition
Special Issues, Collections and Topics in MDPI journals

Dr. Zhuyun Chen

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Guest Editor

School of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou 510640, China
Interests: deep transfer learning; federated learning; signal processing; fault diagnosis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Nowadays, with the rapid advancement of artificial intelligence (AI), various AI techniques have been applied to ensure equipment and production reliability, safety, and quality, and to prevent unexpected failures in the smart manufacturing. The boosting of the applications of AI techniques brings new opportunities in the smart manufacturing, including intelligent fault diagnosis, prognosis, and surface defect detection. These AI-assisted techniques can usually handle various industrial signals or images to monitor the health of the machines or the products, which have shown a great potential to improve the safety and efficiency of the smart manufacturing. The proposed Special Issue on Artificial Intelligence-Assisted Diagnosis Techniques in Smart Manufacturing focus on the theories, the methodologies, as well as the applications of AI techniques in smart manufacturing. Researchers can use various industrial data (such as signals, images, or videos) to diagnose and predict the state of the machines or the products. The aim of this Special Issue is to present the current innovations and reflect the latest development of AI-assisted diagnosis techniques and their applications in smart manufacturing. Potential topics for submissions include, but are not limited to, the following:

AI-assisted prognostics and health management (PHM);
Intelligent fault diagnosis and prognosis techniques;
Advanced machine learning methods in industrial surface defect detection;
Data analytics and information fusion for condition monitoring and predictive maintenance;
Sensor data fusion in manufacturing process and product quality monitoring;
Interoperable AI architecture and techniques of manufacturing applications;
The development of data-driven, physics-based, or hybrid methods for industrial maintenance.

Prof. Dr. Long Wen
Prof. Dr. Haidong Shao
Prof. Dr. Xinyu Li
Dr. Zhuyun Chen
Guest Editors

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Keywords

sensor data fusion
industrial signals
Artificial Intelligence
fault diagnosis
surface defect detection
advanced machine learning

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Related Special Issue

Artificial-Intelligence-Driven Intelligent Fault Prediction and Health Management Techniques in Manufacturing Systems: 2nd Edition in Sensors (6 articles)

Published Papers (9 papers)

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Research

26 pages, 10003 KiB

Open AccessArticle

Metric Learning-Guided Semi-Supervised Path-Interaction Fault Diagnosis Method for Extremely Limited Labeled Samples under Variable Working Conditions

by Zheng Yang, Fei Chen, Binbin Xu, Boquan Ma, Zege Qu and Xin Zhou

Sensors 2023, 23(15), 6951; https://doi.org/10.3390/s23156951 - 4 Aug 2023

Cited by 1 | Viewed by 1244

Abstract

The lack of labeled data and variable working conditions brings challenges to the application of intelligent fault diagnosis. Given this, extracting labeled information and learning distribution-invariant representation provides a feasible and promising way. Enlightened by metric learning and semi-supervised architecture, a triplet-guided path-interaction ladder network (Tri-CLAN) is proposed based on the aspects of algorithm structure and feature space. An encoder–decoder structure with path interaction is built to utilize the unlabeled data with fewer parameters, and the network structure is simplified by CNN and an element additive combination activation function. Metric learning is introduced to the feature space of the established algorithm structure, which enables the mining of hard samples from extremely limited labeled data and the learning of working condition-independent representations. The generalization and applicability of Tri-CLAN are proved by experiments, and the contribution of the algorithm structure and the metric learning in the feature space are discussed. Full article

(This article belongs to the Special Issue Artificial Intelligence Assisted Diagnosis Techniques in Smart Manufacturing)

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25 pages, 19085 KiB

Open AccessArticle

GRU-Based Denoising Autoencoder for Detection and Clustering of Unknown Single and Concurrent Faults during System Integration Testing of Automotive Software Systems

by Mohammad Abboush, Christoph Knieke and Andreas Rausch

Sensors 2023, 23(14), 6606; https://doi.org/10.3390/s23146606 - 22 Jul 2023

Cited by 7 | Viewed by 2457

Abstract

Recently, remarkable successes have been achieved in the quality assurance of automotive software systems (ASSs) through the utilization of real-time hardware-in-the-loop (HIL) simulation. Based on the HIL platform, safe, flexible and reliable realistic simulation during the system development process can be enabled. However, notwithstanding the test automation capability, large amounts of recordings data are generated as a result of HIL test executions. Expert knowledge-based approaches to analyze the generated recordings, with the aim of detecting and identifying the faults, are costly in terms of time, effort and difficulty. Therefore, in this study, a novel deep learning-based methodology is proposed so that the faults of automotive sensor signals can be efficiently and automatically detected and identified without human intervention. Concretely, a hybrid GRU-based denoising autoencoder (GRU-based DAE) model with the k-means algorithm is developed for the fault-detection and clustering problem in sequential data. By doing so, based on the real-time historical data, not only individual faults but also unknown simultaneous faults under noisy conditions can be accurately detected and clustered. The applicability and advantages of the proposed method for the HIL testing process are demonstrated by two automotive case studies. To be specific, a high-fidelity gasoline engine and vehicle dynamic system along with an entire vehicle model are considered to verify the performance of the proposed model. The superiority of the proposed architecture compared to other autoencoder variants is presented in the results in terms of reconstruction error under several noise levels. The validation results indicate that the proposed model can perform high detection and clustering accuracy of unknown faults compared to stand-alone techniques. Full article

(This article belongs to the Special Issue Artificial Intelligence Assisted Diagnosis Techniques in Smart Manufacturing)

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17 pages, 10566 KiB

Open AccessArticle

LSD-YOLOv5: A Steel Strip Surface Defect Detection Algorithm Based on Lightweight Network and Enhanced Feature Fusion Mode

by Huan Zhao, Fang Wan, Guangbo Lei, Ying Xiong, Li Xu, Chengzhi Xu and Wen Zhou

Sensors 2023, 23(14), 6558; https://doi.org/10.3390/s23146558 - 20 Jul 2023

Cited by 20 | Viewed by 3583

Abstract

In the field of metallurgy, the timely and accurate detection of surface defects on metallic materials is a crucial quality control task. However, current defect detection approaches face challenges with large model parameters and low detection rates. To address these issues, this paper proposes a lightweight recognition model for surface damage on steel strips, named LSD-YOLOv5. First, we design a shallow feature enhancement module to replace the first Conv structure in the backbone network. Second, the Coordinate Attention mechanism is introduced into the MobileNetV2 bottleneck structure to maintain the lightweight nature of the model. Then, we propose a smaller bidirectional feature pyramid network (BiFPN-S) and combine it with Concat operation for efficient bidirectional cross-scale connectivity and weighted feature fusion. Finally, the Soft-DIoU-NMS algorithm is employed to enhance the recognition efficiency in scenarios where targets overlap. Compared with the original YOLOv5s, the LSD-YOLOv5 model achieves a reduction of 61.5% in model parameters and a 28.7% improvement in detection speed, while improving recognition accuracy by 2.4%. This demonstrates that the model achieves an optimal balance between detection accuracy and speed, while maintaining a lightweight structure. Full article

(This article belongs to the Special Issue Artificial Intelligence Assisted Diagnosis Techniques in Smart Manufacturing)

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23 pages, 5755 KiB

Open AccessArticle

Fault Diagnosis of Rotating Machinery: A Highly Efficient and Lightweight Framework Based on a Temporal Convolutional Network and Broad Learning System

by Hao Wei, Qinghua Zhang and Yu Gu

Sensors 2023, 23(12), 5642; https://doi.org/10.3390/s23125642 - 16 Jun 2023

Cited by 2 | Viewed by 1521

Abstract

Efficient fault diagnosis of rotating machinery is essential for the safe operation of equipment in the manufacturing industry. In this study, a robust and lightweight framework consisting of two lightweight temporal convolutional network (LTCN) backbones and a broad learning system with incremental learning (IBLS) classifier called LTCN-IBLS is proposed for the fault diagnosis of rotating machinery. The two LTCN backbones extract the fault’s time–frequency and temporal features with strict time constraints. The features are fused to obtain more comprehensive and advanced fault information and input into the IBLS classifier. The IBLS classifier is employed to identify the faults and exhibits a strong nonlinear mapping ability. The contributions of the framework’s components are analyzed by ablation experiments. The framework’s performance is verified by comparing it with other state-of-the-art models using four evaluation metrics (accuracy, macro-recall (MR), macro-precision (MP), and macro-F1 score (MF)) and the number of trainable parameters on three datasets. Gaussian white noise is introduced into the datasets to evaluate the robustness of the LTCN-IBLS. The results show that our framework provides the highest mean values of the evaluation metrics (accuracy ≥ 0.9158, MP ≥ 0.9235, MR ≥ 0.9158, and MF ≥ 0.9148) and the lowest number of trainable parameters (≤0.0165 Mage), indicating its high effectiveness and strong robustness for fault diagnosis. Full article

(This article belongs to the Special Issue Artificial Intelligence Assisted Diagnosis Techniques in Smart Manufacturing)

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15 pages, 7839 KiB

Open AccessArticle

Nonlinear and Dotted Defect Detection with CNN for Multi-Vision-Based Mask Inspection

by Jimyeong Woo and Heoncheol Lee

Sensors 2022, 22(22), 8945; https://doi.org/10.3390/s22228945 - 18 Nov 2022

Cited by 2 | Viewed by 3093

Abstract

This paper addresses the problem of nonlinear and dotted defect detection for multi-vision-based mask inspection systems in mask manufacturing lines. As the mask production amounts increased due to the spread of COVID-19 around the world, the mask inspection systems require more efficient defect detection algorithms. However, the traditional computer vision detection algorithms suffer from various types and very small sizes of the nonlinear and dotted defects on masks. This paper proposes a deep learning-based mask defect detection method, which includes a convolutional neural network (CNN) and efficient preprocessing. The proposed method was developed to be applied to real manufacturing systems, and thus all the training and inference processes were conducted with real data produced by real mask manufacturing systems. Experimental results show that the nonlinear and dotted defects were successfully detected by the proposed method, and its performance was higher than the previous method. Full article

(This article belongs to the Special Issue Artificial Intelligence Assisted Diagnosis Techniques in Smart Manufacturing)

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14 pages, 3880 KiB

Open AccessArticle

Integrated Gradient-Based Continuous Wavelet Transform for Bearing Fault Diagnosis

by Junfei Du, Xinyu Li, Yiping Gao and Liang Gao

Sensors 2022, 22(22), 8760; https://doi.org/10.3390/s22228760 - 12 Nov 2022

Cited by 34 | Viewed by 3001

Abstract

Bearing fault diagnosis is important to ensure safe operation and reduce loss for most rotating machinery. In recent years, deep learning (DL) has been widely used for bearing fault diagnosis and has achieved excellent results. Continuous wavelet transform (CWT), which can convert original sensor data to time–frequency images, is often used to preprocess vibration data for the DL model. However, in time–frequency images, some frequency components may be important, and some may be unimportant for DL models for fault diagnosis. So, how to choose a frequency range of important frequency components is needed for CWT. In this paper, an Integrated Gradient-based continuous wavelet transform (IG-CWT) method is proposed to address this issue. Through IG-CWT, the important frequency components and the component frequency range can be detected and used for data preprocessing. To verify our method, experiments are conducted on four famous bearing datasets using 3 DL models, separately, and compared with CWT, and the results are compared with the original CWT. The comparisons show that the proposed IG-CWT can achieve higher fault diagnosis accuracy. Full article

(This article belongs to the Special Issue Artificial Intelligence Assisted Diagnosis Techniques in Smart Manufacturing)

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17 pages, 8185 KiB

Open AccessArticle

An Energy Data-Driven Approach for Operating Status Recognition of Machine Tools Based on Deep Learning

by Wei Yan, Chenxun Lu, Ying Liu, Xumei Zhang and Hua Zhang

Sensors 2022, 22(17), 6628; https://doi.org/10.3390/s22176628 - 1 Sep 2022

Cited by 2 | Viewed by 2092

Abstract

Machine tools, as an indispensable equipment in the manufacturing industry, are widely used in industrial production. The harsh and complex working environment can easily cause the failure of machine tools during operation, and there is an urgent requirement to improve the fault diagnosis ability of machine tools. Through the identification of the operating state (OS) of the machine tools, defining the time point of machine tool failure and the working energy-consuming unit can be assessed. In this way, the fault diagnosis time of the machine tool is shortened and the fault diagnosis ability is improved. Aiming at the problems of low recognition accuracy, slow convergence speed and weak generalization ability of traditional OS recognition methods, a deep learning method based on data-driven machine tool OS recognition is proposed. Various power data (such as signals or images) of CNC machine tools can be used to recognize the OS of the machine tool, followed by an intuitive judgement regarding whether the energy-consuming units included in the OS are faulty. First, the power data are collected, and the data are preprocessed by noise reduction and cropping using the data preprocessing method of wavelet transform (WT). Then, an AlexNet Convolutional Neural Network (ACNN) is built to identify the OS of the machine tool. In addition, a parameter adaptive adjustment mechanism of the ACNN is studied to improve identification performance. Finally, a case study is presented to verify the effectiveness of the proposed approach. To illustrate the superiority of this method, the approach was compared with traditional classification methods, and the results reveal the superiority in the recognition accuracy and computing speed of this AI technology. Moreover, the technique uses power data as a dataset, and also demonstrates good progress in portability and anti-interference. Full article

(This article belongs to the Special Issue Artificial Intelligence Assisted Diagnosis Techniques in Smart Manufacturing)

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30 pages, 3221 KiB

Open AccessArticle

An Improved Entropy-Weighted Topsis Method for Decision-Level Fusion Evaluation System of Multi-Source Data

by Lilan Liu, Xiang Wan, Jiaying Li, Wenxi Wang and Zenggui Gao

Sensors 2022, 22(17), 6391; https://doi.org/10.3390/s22176391 - 25 Aug 2022

Cited by 15 | Viewed by 4075

Abstract

Due to the rapid development of industrial internet technology, the traditional manufacturing industry is in urgent need of digital transformation, and one of the key technologies to achieve this is multi-source data fusion. For this problem, this paper proposes an improved entropy-weighted topsis method for a multi-source data fusion evaluation system. It adds a fusion evaluation system based on the decision-level fusion algorithm and proposes a dynamic fusion strategy. The fusion evaluation system effectively solves the problem of data scale inconsistency among multi-source data, which leads to difficulties in fusing models and low fusion accuracy, and obtains optimal fusion results. The paper then verifies the effectiveness of the fusion evaluation system through experiments on the multilayer feature fusion of single-source data and the decision-level fusion of multi-source data, respectively. The results of this paper can be used in intelligent production and assembly plants in the discrete industry and provide the corresponding management and decision support with a certain practical value. Full article

(This article belongs to the Special Issue Artificial Intelligence Assisted Diagnosis Techniques in Smart Manufacturing)

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27 pages, 7135 KiB

Open AccessArticle

Bearing Fault Feature Extraction Method Based on Enhanced Differential Product Weighted Morphological Filtering

by Xiaoan Yan, Tao Liu, Mengyuan Fu, Maoyou Ye and Minping Jia

Sensors 2022, 22(16), 6184; https://doi.org/10.3390/s22166184 - 18 Aug 2022

Cited by 13 | Viewed by 2239

Abstract

Aimed at the problem of fault characteristic information bearing vibration signals being easily submerged in some background noise and harmonic interference, a new algorithm named enhanced differential product weighted morphological filtering (EDPWMF) is proposed for bearing fault feature extraction. In this method, an enhanced differential product weighted morphological operator (EDPWO) is first constructed by means of infusing the differential product operation and weighted operation into four basic combination morphological operators. Subsequently, aiming at the disadvantage of the parameter selection of the structuring element (SE) of EDPWO depending on artificial experience, an index named fault feature ratio (FFR) is employed to automatically determine the flat SE length of EDPWO and search for the optimal weighting correlation factors. The fault diagnosis results of simulation signals and experimental bearing fault signals show that the proposed method can effectively extract bearing fault feature information from raw bearing vibration signals containing noise interference. Moreover, the filtering result obtained by the proposed method is better than that of traditional morphological filtering methods (e.g., AVG, STH and EMDF) through comparative analysis. This study provides a reference value for the construction of advanced morphological analysis methods. Full article

(This article belongs to the Special Issue Artificial Intelligence Assisted Diagnosis Techniques in Smart Manufacturing)

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