With the continuous increase in complexity and expense of industrial systems, there is less tolerance for performance degradation, productivity decrease, and safety hazards, which greatly necessitates to detect and identify any kinds of potential abnormalities and faults as early as possible and implement real-time fault-tolerant operation for minimizing performance degradation and avoiding dangerous situations. During the last four decades, fruitful results have been reported about fault diagnosis and fault-tolerant control methods and their applications in a variety of engineering systems. The three-part survey paper aims to give a comprehensive review of real-time fault diagnosis and fault-tolerant control, with particular attention on the results reported in the last decade. In this paper, fault diagnosis approaches and their applications are comprehensively reviewed from model- and signal-based perspectives, respectively.

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A Survey of Fault Diagnosis and Fault-Tolerant Techniques—Part I: Fault Diagnosis With Model-Based and Signal-Based Approaches

Statistics and Data Analysis in Geology

Fault diagnosis is vital in manufacturing system, since early detections on the emerging problem can save invaluable time and cost. With the development of smart manufacturing, the data-driven fault diagnosis becomes a hot topic. However, the traditional data-driven fault diagnosis methods rely on the features extracted by experts. The feature extraction process is an exhausted work and greatly impacts the final result. Deep learning (DL) provides an effective way to extract the features of raw data automatically. Convolutional neural network (CNN) is an effective DL method. In this study, a new CNN based on LeNet-5 is proposed for fault diagnosis. Through a conversion method converting signals into two-dimensional (2-D) images, the proposed method can extract the features of the converted 2-D images and eliminate the effect of handcrafted features. The proposed method which is tested on three famous datasets, including motor bearing dataset, self-priming centrifugal pump dataset, and axial piston hydraulic pump dataset, has achieved prediction accuracy of 99.79%, 99.481%, and 100%, respectively. The results have been compared with other DL and traditional methods, including adaptive deep CNN, sparse filter, deep belief network, and support vector machine. The comparisons show that the proposed CNN-based data-driven fault diagnosis method has achieved significant improvements.

A New Convolutional Neural Network-Based Data-Driven Fault Diagnosis Method

Intelligent fault diagnosis is a promising tool to deal with mechanical big data due to its ability in rapidly and efficiently processing collected signals and providing accurate diagnosis results. In traditional intelligent diagnosis methods, however, the features are manually extracted depending on prior knowledge and diagnostic expertise. Such processes take advantage of human ingenuity but are time-consuming and labor-intensive. Inspired by the idea of unsupervised feature learning that uses artificial intelligence techniques to learn features from raw data, a two-stage learning method is proposed for intelligent diagnosis of machines. In the first learning stage of the method, sparse filtering, an unsupervised two-layer neural network, is used to directly learn features from mechanical vibration signals. In the second stage, softmax regression is employed to classify the health conditions based on the learned features. The proposed method is validated by a motor bearing dataset and a locomotive bearing dataset, respectively. The results show that the proposed method obtains fairly high diagnosis accuracies and is superior to the existing methods for the motor bearing dataset. Because of learning features adaptively, the proposed method reduces the need of human labor and makes intelligent fault diagnosis handle big data more easily.

An Intelligent Fault Diagnosis Method Using Unsupervised Feature Learning Towards Mechanical Big Data

Regular inspection of nuclear power plant components is important to guarantee safe operations. However, current practice is time consuming, tedious, and subjective, which involves human technicians reviewing the inspection videos and identifying cracks on reactors. A few vision-based crack detection approaches have been developed for metallic surfaces, and they typically perform poorly when used for analyzing nuclear inspection videos. Detecting these cracks is a challenging task since they are tiny, and noisy patterns exist on the components’ surfaces. This study proposes a deep learning framework, based on a convolutional neural network (CNN) and a Naive Bayes data fusion scheme, called NB-CNN, to analyze individual video frames for crack detection while a novel data fusion scheme is proposed to aggregate the information extracted from each video frame to enhance the overall performance and robustness of the system. To this end, a CNN is proposed to detect crack patches in each video frame, while the proposed data fusion scheme maintains the spatiotemporal coherence of cracks in videos, and the Naive Bayes decision making discards false positives effectively. The proposed framework achieves a 98.3% hit rate against 0.1 false positives per frame that is significantly higher than state-of-the-art approaches as presented in this paper.

NB-CNN: Deep Learning-Based Crack Detection Using Convolutional Neural Network and Naïve Bayes Data Fusion

Coronavirus Disease 2019 (COVID-19) still presents a pandemic trend globally. Detecting infected individuals and analyzing their status can provide patients with proper healthcare while protecting the normal population. Chest CT (computed tomography) is an effective tool for screening of COVID-19. It displays detailed pathology-related information. To achieve automated COVID-19 diagnosis and lung CT image segmentation, convolutional neural networks (CNNs) have become mainstream methods. However, most of the previous works consider automated diagnosis and image segmentation as two independent tasks, in which some focus on lung fields segmentation and the others focus on single-lesion segmentation. Moreover, lack of clinical explainability is a common problem for CNN-based methods. In such context, we develop a multi-task learning framework in which the diagnosis of COVID-19 and multi-lesion recognition (segmentation of CT images) are achieved simultaneously. The core of the proposed framework is an explainable multi-instance multi-task network. The network learns task-related features adaptively with learnable weights, and gives explicable diagnosis results by suggesting local CT images with lesions as additional evidence. Then, severity assessment of COVID-19 and lesion quantification are performed to analyze patient status. Extensive experimental results on real-world datasets show that the proposed framework outperforms all the compared approaches for COVID-19 diagnosis and multi-lesion segmentation. 

https://doi.org/10.1016/j.knosys.2022.109278

Explainable multi-instance and multi-task learning for COVID-19 diagnosis and lesion segmentation in CT images

This paper presents the latest study on the data-driven process monitoring system design for the dynamic processes with sinusoidal disturbance. In the previous study, it is understood that the row space of the deterministic disturbance is essential to the subspace method aided data-driven design. Based on the previous study, this paper first determines the row space of sinusoidal disturbance. By projecting the process data into the determined subspaces, the fault detection systems can be designed based on the identified kernel subspace of the system. The performance and effectiveness of the proposed scheme are verified and demonstrated through the numerical study on randomly generated systems.

A Data-Driven Fault Detection Approach for Dynamic Processes with Sinusoidal Disturbance

BACKGROUND
Accurate and automated brain tumor segmentation from multi-modality MR images plays a significant role in tumor treatment. However, the existing approaches mainly focus on the fusion of multi-modality while ignoring the correlation between single-modality and tumor sub-components. For example, T2-weighted images show good visualization of edema, and T1-contrast images have a good contrast between enhancing tumor core and necrosis. In the actual clinical process, professional physicians also label tumors according to these characteristics. We design a method for brain tumors segmentation that utilizes both multi-modality fusion and single-modality characteristics.


METHODS
A multi-modality and single-modality feature recalibration network (MSFR-Net) is proposed for brain tumor segmentation from MR images. Specifically, multi-modality information and single-modality information are assigned to independent pathways. Multi-modality network explicitly learn the relationship between all modalities and all tumor sub-components. Single-modality network learn the relationship between single-modality and its highly correlated tumor sub-components. Then, a dual recalibration module (DRM) is designed to connect the parallel single-modality network and multi-modality network at multiple stages. The function of the DRM is to unify the two types of features into the same feature space.


RESULTS
Experiments on BraTS 2015 dataset and BraTS 2018 dataset show that the proposed method is competitive and superior to other state-of-the-art methods. The proposed method achieved the segmentation results with dice coefficients of 0.86 and hausdorff distance of 4.82 on BraTS 2018 dataset, with dice coefficients of 0.80, positive predictive value of 0.76 and sensitivity of 0.78 on BraTS 2015 dataset.


CONCLUSIONS
This work combines the manual labeling process of doctors and introduces the correlation between single-modality and the tumor sub-components into the segmentation network. The method improves the segmentation performance of brain tumors and can be applied in the clinical practice. The code of the proposed method is available at: https://github.com/xiangQAQ/MSFR-Net. This article is protected by copyright. All rights reserved.

Hao Luo

Papers

Explainable multi-instance and multi-task learning for COVID-19 diagnosis and lesion segmentation in CT images

A Data-Driven Fault Detection Approach for Dynamic Processes with Sinusoidal Disturbance

MSFR-Net: Multi-modality and single-modality feature recalibration network for brain tumor segmentation.

Robust Just-in-time Learning Approach and Its Application on Fault Detection

Recursive Subspace-based Predictive Control and Its Application to Fault-tolerant Control