Fault diagnosis based on extremely randomized trees in wireless sensor networks

The problem of the wind turbine gearbox fault diagnosis was investigated by employing the support vector machine (SVM), which is optimized by the improved fruit fly intelligent algorithm with decreasing steps. First of all, the fault characteristic value extracted by Hilbert transform envelope is presented. Secondly, the general SVM solution to the wind turbine gearbox fault diagnosis problem is presented, where the improved fruit fly intelligent algorithm is adopted to optimize the performance of the model of the fault detection and diagnosis. Thirdly, the effectiveness of three fault diagnosis models are compared, including the fault detection and diagnosis model by the traditional SVM, its improved model optimized by the partical swarm optimization algorithm, and improved model by the SVM optimized by the proposed approach. By using the proposed optimization algorithm, the accuracy of gearbox fault diagnosis is much better than other two models, which are validated by the extensive simulation results based on practical historical operation data.

Wireless Sensor Networks (WSNs) have emerged as crucial technologies for various applications, including environmental monitoring, healthcare, and smart cities. However, maintaining the reliability and performance of WSNs is challenging due to faults such as node failures, communication issues, and environmental factors. Machine learning (ML) techniques offer promising solutions for diagnosing and predicting faults in WSNs. This article explores various ML-based fault diagnosis methods, highlighting supervised, unsupervised, and hybrid approaches, and their applications in WSNs. The paper also discusses the advantages, limitations, and future directions for ML in fault diagnosis for WSNs.

The rapid growth of the Internet and information technologies and also the diminution of the price of hardware components like wireless sensors leads to the fast growth in Wireless Sensor Network (WSN). The WSN is a bunch/group of sensors that are located across the area, in order to monitor and determine environmental conditions such as temperature, humidity, vibrations, wind, water levels, pollution levels. WSNs are susceptible to major attacks like Denial of Service (DOS), Wormhole attack, Sinkhole attack, etc. The main threat to the WSN is because of the broadcast nature of the nodes in the network. Therefore, the security of WSNs is the essential part that must be done. Hence, to overcome these problems or threats, we are trying to detect it using AI technology. With the expanding fields of Machine Learning and Deep Learning, we can apply various algorithms in order to classify different types of attacks. Once we detect the attack properly, we can prevent it accordingly. We are using WSN-DS. It has 4 classes of attacks which are Grayhole, Blackhole, TDMA(Scheduling), and Flooding which comes under the category of DOS attacks. In this chapter, we have analysed and compared the accuracies of 5 main machine learning classification algorithms. Also, we have analysed the 1 deep learning algorithm. The ANN (Artificial Neural Network) and 5 machine learning algorithms have been trained on the dataset. Furthermore, we have used K-fold cross-validation to get even more accurate predictions. After analysis of these algorithms, we came to the point that, the Machine Learning algorithms like Random Forest, Support Vector Machines and Deep Learning algorithm, namely, Artificial Neural Network can help us for detecting intrusions in the system or network. This will help researchers in designing their own machine learning model on top of our suggested model.

The speed and localization information of the high-speed trains are essential and significant for train control systems. The occurrence of faults in SDUs (Speed Distance Units) will affect the accuracy and effectiveness of location reports to ensure the operation safety of high-speed trains. The fault detection and diagnosis method for SDUs are focused in this paper. By using the on-board GPS (Global Positioning System) measurements, we propose a parallel-filtering-based architecture for detecting the faults in odometers that use the pulse-counting information to estimate the speed and travelling distance of the trains. According to the characteristics of different fault modes, including the hard fault, soft fault and implicated fault, a rule-based fault diagnosis approach is proposed to identify the detected fault and take suggested maintenance measures, where a conservative determination strategy for utilizing the results of the two filters is demonstrated to decrease the missed detection probability. The field operation data from the high-speed train is employed to build the simulation scenarios. The simulation results demonstrate the performance of the proposed method and illustrate its potentials in real-time fault diagnosis and maintenance for speed distance units.

PREFACE. ACKNOWLEDGMENTS. PROLOGUE. 1 INTRODUCTION. 1.1 Historical Perspective. 1.2 Diagnostic and Prognostic System Requirements. 1.3 Designing in Fault Diagnostic and Prognostic Systems. 1.4 Diagnostic and Prognostic Functional Layers. 1.5 Preface to Book Chapters. 1.6 References. 2 SYSTEMS APPROACH TO CBM/PHM. 2.1 Introduction. 2.2 Trade Studies. 2.3 Failure Modes and Effects Criticality Analysis (FMECA). 2.4 System CBM Test-Plan Design. 2.5 Performance Assessment. 2.6 CBM/PHM Impact on Maintenance and Operations: Case Studies. 2.7 CBM/PHM in Control and Contingency Management. 2.8 References. 3 SENSORS AND SENSING STRATEGIES. 3.1 Introduction. 3.2 Sensors. 3.3 Sensor Placement. 3.4 Wireless Sensor Networks. 3.5 Smart Sensors. 3.6 References. 4 SIGNAL PROCESSING AND DATABASE MANAGEMENT SYSTEMS. 4.1 Introduction. 4.2 Signal Processing in CBM/PHM. 4.3 Signal Preprocessing. 4.4 Signal Processing. 4.5 Vibration Monitoring and Data Analysis. 4.6 Real-Time Image Feature Extraction and Defect/Fault Classification. 4.7 The Virtual Sensor. 4.8 Fusion or Integration Technologies. 4.9 Usage-Pattern Tracking. 4.10 Database Management Methods. 4.11 References. 5 FAULT DIAGNOSIS. 5.1 Introduction. 5.2 The Diagnostic Framework. 5.3 Historical Data Diagnostic Methods. 5.4 Data-Driven Fault Classification and Decision Making. 5.5 Dynamic Systems Modeling. 5.6 Physical Model-Based Methods. 5.7 Model-Based Reasoning. 5.8 Case-Based Reasoning (CBR). 5.9 Other Methods for Fault Diagnosis. 5.10 A Diagnostic Framework for Electrical/Electronic Systems. 5.11 Case Study: Vibration-Based Fault Detection and Diagnosis for Engine Bearings. 5.12 References. 6 FAULT PROGNOSIS. 6.1 Introduction. 6.2 Model-Based Prognosis Techniques. 6.3 Probability-Based Prognosis Techniques. 6.4 Data-Driven Prediction Techniques. 6.5 Case Studies. 6.6 References. 7 FAULT DIAGNOSIS AND PROGNOSIS PERFORMANCE METRICS. 7.1 Introduction. 7.2 CBM/PHM Requirements Definition. 7.3 Feature-Evaluation Metrics. 7.4 Fault Diagnosis Performance Metrics. 7.5 Prognosis Performance Metrics. 7.6 Diagnosis and Prognosis Effectiveness Metrics. 7.7 Complexity/Cost-Benefit Analysis of CBM/PHM Systems. 7.8 References. 8 LOGISTICS: SUPPORT OF THE SYSTEM IN OPERATION. 8.1 Introduction. 8.2 Product-Support Architecture, Knowledge Base, and Methods for CBM. 8.3 Product Support without CBM. 8.4 Product Support with CBM. 8.5 Maintenance Scheduling Strategies. 8.6 A Simple Example. 8.7 References. APPENDIX. INDEX.

SummaryA fault diagnosis and compensation problem for two‐dimensional discrete time systems with time‐varying state delays is studied in this paper. The concerned two‐dimensional systems are described by the Fornasisi–Marchesini second model and are subject to unknown disturbances. First, a fault detection and diagnosis module is designed to obtain the information on sensor faults; a new fault detection and diagnosis integrated design, using the observer based on descriptor system approach, is proposed to detect and estimate the sensor faults. The integrated design can maximize the fault detection rate for a given false alarm rate. Sufficient conditions for the existence of the integrated fault detection and diagnosis design are derived in the context of norm evaluation and provided in terms of matrix inequalities. Second, a fault‐tolerant control module is proposed upon an existing output feedback controller. When the sensor fault occurs, the faulty measurement can be identified and corrected by the proposed fault detection and diagnosis module. In this case, the feedback controller can guarantee the performance of the closed‐loop system even when encountering sensor faults. Finally, the proposed method is applied to a thermal process to illustrate its effectiveness. Copyright © 2017 John Wiley & Sons, Ltd.

Recursive Actuator Fault Detection and Diagnosis for Emergency Landing of UASs

This paper considers the problem of jointly constructing and scheduling forwarding trees in a wireless sensor network, each for a group of sensor nodes, to collect measurements at a single sink node. The goal is to construct such trees which gather measurements in the most energy efficient manner and minimal gathering latency. We assume transmissions (carrying measurements) on wireless links interfere with one another, and thus appropriate link scheduling is required to overcome interference. We refer to this problem as Forwarding Tree Construction and Scheduling (FTCS). Each tree may be constructed independently and then its links are scheduled. However, when all trees are combined together, the shortest and energy efficient schedule may not be guaranteed. Further, a large number of possible forwarding trees for each group of sensors may be considered. Both problems of enumerating forwarding trees and scheduling links for those trees are hard combinatorial problems. This is compounded by the fact that the two problems must be solved jointly, to guarantee the selection of best forwarding trees which, when their links are scheduled, guarantee a shortest energy efficient schedule. After highlighting the complexity of the FTCS problem, we present a novel primal-dual decomposition method using column generation. We also highlight several challenges we faced when solving the decomposed problem and present efficient techniques for mitigating those challenges. One major advantage of our work is that it can serve as a benchmark for evaluating the performance of any low complexity method for solving the FTCS problem for larger network instances where no known exact solutions can be found.

Echo-Aided Bat Algorithm is proved a good evolutionary computing to solve continuous problems in previous investigate. In this study, EABA is applied to solve the discrete problem that construct a network topology with spanning tree in wireless sensor networks (WSNs). In this application, the presentation of bat and design of fitness function perhaps affect the evolutionary results. For the demonstration of simulated results, EABA still presents a satisfied performance in some scenarios designed in this study. In addition, the constructed network topology of spanning tree based on global optimum solution found by EABA is used to estimate the network lifetime. Overall, this framework including network topology constructed by EABD and network lifetime estimated by transmission simulation is created in this study to aid the plan for duration of WSN deployment.

Fault detection and diagnosis has an effective role for the safe operation and long life of systems. Condition monitoring is an appropriate way of the maintenance techniques which is applicable in the fault diagnosis of rotating machinery faults. We considered the Support Vector Machine (SVM) method for classifying the condition of centrifugal pump into two types of faults through six features: flow, temperature, suction pressure, discharge pressure, velocity, and vibration. The SVM method is based on statistical learning theory (SLT) and powerful for the problem with small sampling, nonlinear and high dimension. (L.V. Ganyun et al 2005). The SVM classifying is implemented with 4 kernel functions and the results of them are compared. We use an Artificial Neural Network (ANN) as the second classifying method to have comparison among the performance of two methods. After applying the two methods to our data set we make the data set noisy and again we try our SVMs and ANN to compare their robustness in noisy conditions and the results obtained from two methods confirmed the superiority of SVM with some specific kernel functions.

The electrical system fault testing and diagnosis system based on I2C-bus and embedded equipment is proposed. This system is accomplished with the integration of PXI-based computer, field fault testing platform, dedicated interface adapter unit and accessories with software program of virtual instrument and database management and fault guidance system. It can obtain various state parameters of the heavy mechanized bridge by interface adapter unit and corresponding sensors. The dedicated interface adapter unit, that is the core of the hardware platform is composed of chassis, embedded microcomputer controller, I2C-bus data acquisition modules, VPC connection package and military aviation sockets. Because of its flexibility and modular design, it can support different data conversion, for example, analog, digital, and serial, while functioning as an intelligent conversion bridge between field fault testing platform or PXI computer and electrical system of heavy mechanized bridge electrical system. The fault diagnosis software is of hierarchical modular structure with integration of system management control, fault detection and circuit hardware driver modules together with fault repair and diagnosis database. Therefore it provides functions of human-machine interaction, equipment fault detection, fault diagnosis and analysis, as well as repair and maintenance guidance The fault testing and diagnosis system in this work is also featured with handy structure, lightweight, low consumption, large storage capacity, excellent expandability and intelligent inference capability. The system can be used in the online fault testing and diagnosis of similar engineering equipments. So it has important engineering and military application value to the rapid fault testing and fault diagnosis of engineering equipments.

Undernutrition among children under the age of five is a major public health concern, especially in developing countries. This study aimed to use machine learning (ML) algorithms to predict undernutrition and identify its associated factors. Secondary data analysis of the 2017 Multiple Indicator Cluster Survey (MICS) was performed using R and Python. The main outcomes of interest were undernutrition (stunting: height-for-age (HAZ) < -2 SD; wasting: weight-for-height (WHZ) < -2 SD; and underweight: weight-for-age (WAZ) < -2 SD). Seven ML algorithms were trained and tested: linear discriminant analysis (LDA), logistic model, support vector machine (SVM), random forest (RF), least absolute shrinkage and selection operator (LASSO), ridge regression, and extreme gradient boosting (XGBoost). The ML models were evaluated using the accuracy, confusion matrix, and area under the curve (AUC) receiver operating characteristics (ROC). In total, 8564 children were included in the final analysis. The average age of the children was 926 days, and the majority were females. The weighted prevalence rates of stunting, wasting, and underweight were 17%, 7%, and 12%, respectively. The accuracies of all the ML models for wasting were (LDA: 84%; Logistic: 95%; SVM: 92%; RF: 94%; LASSO: 96%; Ridge: 84%, XGBoost: 98%), stunting (LDA: 86%; Logistic: 86%; SVM: 98%; RF: 88%; LASSO: 86%; Ridge: 86%, XGBoost: 98%), and for underweight were (LDA: 90%; Logistic: 92%; SVM: 98%; RF: 89%; LASSO: 92%; Ridge: 88%, XGBoost: 98%). The AUC values of the wasting models were (LDA: 99%; Logistic: 100%; SVM: 72%; RF: 94%; LASSO: 99%; Ridge: 59%, XGBoost: 100%), for stunting were (LDA: 89%; Logistic: 90%; SVM: 100%; RF: 92%; LASSO: 90%; Ridge: 89%, XGBoost: 100%), and for underweight were (LDA: 95%; Logistic: 96%; SVM: 100%; RF: 94%; LASSO: 96%; Ridge: 82%, XGBoost: 82%). Age, weight, length/height, sex, region of residence and ethnicity were important predictors of wasting, stunting and underweight. The XGBoost model was the best model for predicting wasting, stunting, and underweight. The findings showed that different ML algorithms could be useful for predicting undernutrition and identifying important predictors for targeted interventions among children under five years in Ghana.

In Wireless Sensor Networks (WSNs), performance and reliability depend on the fault tolerance scheme used in the system. Fault diagnosis is an important part of fault tolerance. An effective diagnosis tool helps network administrators clearly monitor, manage, and troubleshoot the performance of the network. However, the design of online fault diagnosis is crucial in WSNs since many faults can easily happen and propagate. Besides, fault diagnosis put extra burden on the sensor node and it will also consume extra resources of the sensor nodes. Thus, in order to guarantee the network quality of service, it is essential for WSNs to be able to diagnosis faults efficiently. In this paper, we propose an adaptive and efficient approach for fault diagnosis in WSN called (SMART). SMART is a layer independent fault diagnosis service for WSNs. The presented service focuses on diagnosis two types of failures that are likely to happen in WSN deployments which are the node failure due to energy depletion, and the link failure due to poor connectivity with neighbors. From the design view, SMART provides to the application many tunable parameters that make it suitable for various deployment needs: energy-robustness-detection latency tradeoffs, tolerable packet loss, reports frequency etc. Simulation results prove that SMART is resource efficient while providing satisfactory detection and diagnosis accuracy.

Feasibility and improvement of fault detection and diagnosis based on factory-installed sensors for chillers

In this paper we consider a problem of building a forwarding tree for multicast and convergecast traffic in short-range wireless sensor networks. Interference awareness and energy efficiency are the major design objectives for WSN protocols in order to maximize the network lifetime. The existing multicast algorithms aim at constructing low-energy cost trees. Adding interference-awareness, however, leads to increased throughput and further reduces the energy consumption by avoiding unnecessary retransmissions due to interference-induced packet losses. We propose a Localized Area-Spanning Tree (LAST) protocol for wireless short-range sensor networks. Unlike previous similar protocols, the LAST protocol reaches all the nodes in a given geographical area, rather than only specific individual nodes. When creating the tree, the protocol jointly optimizes the energy cost and the interference imposed by the structure.