Neural approach to detecting communication network events

This paper proposes a method based on chaotic spread spectrum sequence for synchronous online diagnosis of intermittent faults for complex and cable. networks, such as the aircraft power system. The novelties of the proposed method include: 1) since chaotic spread spectrum signals are sensitive to initial values, the number of chaotic sequences are not limited and the proposed method is not limited by the number of cables under monitoring; 2) with the self-correlation characteristic and cross-correlation characteristic of chaotic sequence, the proposed method can be extended to online diagnosis of multiple cable intermittent faults; and 3) the signal source impedance amplification method is proposed for intermittent arc faults of multiple cables, which improves the reflection coefficient and increases the accuracy of online detection and location of intermittent arc faults. A series of simulations and experiments on multiple cables with different intermittent fault modes are conducted to verify the effectiveness of the proposed approaches.

For avoiding false-alarm and diagnosing intermittent fault, based on the relationship analysis between intermittent fault and false-alarm, the intermittent fault diagnosis model of power system based on HMM (Hidden Markov Model)-SVM (Support Vector Machine) characteristics was constructed in this paper. By means of this model, the intermittent fault diagnosis procedure of power system based on HMM-SVM characteristics was built. Finally, the intermittent fault diagnosis simulation system of power system based on HMM-SVM characteristics was demonstrated and validated by QUEST software. The result showed that the proposed method was available, and can provided guidance for avoiding false-alarm and diagnosing intermittent fault, and support for stable performance in power system.

Diagnosis of incipient parametric faults has been widely addressed and various model-based as well as data-based methods have been reported in the literature. In contrast to incipient faults, it is unknown if, when and for how long intermittent faults will reappear which makes their diagnosis and prognosis difficult. This paper addresses intermittent faults with a magnitude that increases over time. Such a sequence of intermittent faults may reach a failure alarm threshold and may eventually lead to a component or even a system failure. The proposed approach uses a diagnostic Bond Graph (DBG) model for an online detection, isolation and estimation of intermittent faults. Pulses of increasing height and varying width not equally spaced on the time evolution of residuals of Analytical Redundancy Relations (ARRs) obtained from an offline developed DBG indicate a degradation trend of a parameter or a variable. Certain values of this trend are concurrently extrapolated in a repeated failure prognosis resulting in a sequence of Remaining Useful Life (RUL) estimates. The proposed BG-based approach to a failure prognosis in the presence of intermittent faults can be combined with a previously presented BG-approach to incipient fault prognosis and is applicable to mode switching models. For illustration, it is applied in a case study to a small electronic circuit in an offline simulation. The case study also considers intermittent faults of constant magnitude.

Faulty switches, broken interswitch connections and certain bridging faults in a Benes switching network can be identified using a sequence of faulty paths across the network. The method described may use on-line faulty-path data and is suitable for use in the diagnosis of intermittent faults. It is shown that a single faulty switch can be identified in less than four random faulty paths on average, after presentation of the initial faulty path. The method applies to other types of switching network and provides a similar performance.

A method for the identification of stuck-at bits on a data bus is described. Faulty data words are compared to isolate the stuck-at bits. A similar method can be used to identify an instruction which does not execute correctly in a processor instruction set. Here, faulty programmes, or faulty programme segments, are compared to isolate the faulty instruction. For both bus and instruction fault cases, probabilistic analyses are given with explicit formulas and data for the mean number of faulty samples required for diagnosis, and also the associated standard deviation. Both of these procedures use on-line data and are suitable for the diagnosis of intermittent faults.

Diagnosis of intermittent scan chain failures still remains a hard problem. In this article, we demonstrate that the use of artificial neural networks (ANNs) can lead to significantly higher accuracy. The key of this method is a multistage process incorporating ANNs with gradually refined focuses. During this process, the final fault suspect is elected through multiple rounds of ANN inference, instead of just one round. At each stage, identification of a proper Affine Group, used as the “candidate set of scan cells for the next round of ANN inference,” will influence the final diagnostic accuracy. Thus, we propose a validation-based learning procedure for Affine Group derivation to further boost the final diagnostic accuracy. The experimental results on benchmark circuits have shown that this method is, on the average, 17.46% more accurate than a state-of-the-art commercial tool for intermittent stuck-at-0 faults.

Το αντικείμενο της διατριβής αυτής είναι η μελέτη της διαστρωματικής (cross-layer) προσέγγισης ανάπτυξης ασύρματων δικτύων κατανεμημένης λειτουργίας με απαιτήσεις επικοινωνίας πραγματικού χρόνου και περιορισμένους διαθέσιμους πόρους. Επιπλέον βασικό στόχο αποτελεί και η σχεδίαση, πρόταση αντίστοιχης αρχιτεκτονικής η οποία στοχεύει στη βέλτιστη διαχείριση διαθεσίμων δικτυακών πόρων σε καταστάσεις συμφόρησης του δικτύου και κατά συνέπεια στην βελτίωση της απόδοσης αυτού. Μέσα από μελέτες στα πλαίσια της διατριβής αναδεικνύεται η σημασία του φαινομένου συμφόρησης ως κύριου παράγοντα σπατάλης δικτυακών πόρων καθώς και οδήγησης του δικτύου σε κατάσταση κορεσμού με αρνητική επίπτωση σε όλες τις παραμέτρους απόδοσης του δικτύου. Στόχος, λοιπόν, της προτεινόμενης επικοινωνιακής αρχιτεκτονικής είναι η αποφυγή του φαινομένου συμφόρησης έτσι ώστε το δίκτυο να οδηγείται δυναμικά σε ένα σταθερό σημείο απόδοσης (όσο αυτό είναι δυνατό) το οποίο θα επιτρέπει στο δίκτυο να αποδίδει όσο το δυνατόν καλύτερα αποφεύγοντας τη ίδια στιγμή άσκοπη σπατάλη πόρων. Βασική παράμετρος στη σχεδίαση αποτελεί η συμβατότητα τόσο ως προς σημαντικό εύρος διαθέσιμων πρωτοκόλλων σε διάφορα επίπεδα, όσο και ως προς τη δυνατότητα συνύπαρξης κόμβων που ενσωματώνουν την προτεινόμενη αρχιτεκτονική με κόμβους χωρίς αυτήν. Η προτεινόμενη αρχιτεκτονική υλοποιήθηκε στα πλαίσια γνωστού και αξιόπιστου δικτυακού εξομοίωση. Αξιολόγηση της υλοποίησης αυτής μέσα από μεγάλο αριθμό πειραμάτων έδειξε επίτευξη του στόχου καθώς το φαινόμενο της συμφόρησης αντιμετωπίστηκε σε όλες τις περιπτώσεις με σημαντικά οφέλη στην απόδοση του δικτύου και διαχείριση των πόρων. Επιπλέον, λόγω του μεγάλου βαθμού παραμετροποίησης αποτελεί ιδανική βάση για μελλοντικές προεκτάσεις.

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.

In this paper, the problem of intermittent fault diagnosis for linear discrete systems subjected to unknown disturbance is studied. An interval observer for fault diagnosis is designed, residual evaluation and threshold setting are not required, and the upper and the lower bounds of the residual generated by the interval observer can be directly used as adaptive thresholds for the intermittent sensor fault detection. In order to locate the faults, a set of interval isolation observers is designed to isolate sensor faults. Finally, a simulation example is presented to demonstrate the effectiveness of the proposed diagnosis scheme.

Routing and wavelength assignment vs. EDFA reliability performance in optical backbone networks: An operational cost perspective

Controller Area Networks (CANs) play an important role in many safety-critical industrial systems, which places high demands on their reliability performance. However, the intermittent connection (IC) of network cables, a random and transient connectivity problem, is a common but hard troubleshooting fault that can cause network performance degradation, system-level failures, and even safety issues. Therefore, to ensure the reliability of CANs, a fault symptom association model-based IC fault diagnosis method is proposed. Firstly, the symptoms are defined by examining the error records, and the domains of the symptoms are derived to represent the causal relationship between the fault locations and the symptoms. Secondly, the fault probability for each location is calculated by minimizing the difference between the symptom probabilities calculated from the count information and those fitted by the total probability formula. Then, the fault symptom association model is designed to synthesize the causal and the probabilistic diagnostic information. Finally, a model-based maximal contribution diagnosis algorithm is developed to locate the IC faults. Experimental results of three case studies show that the proposed method can accurately and efficiently identify various IC fault location scenarios in networks.

This paper quantifies the impact of topological characteristics on the performance of single radio multichannel IEEE 802.11 mesh networks. Topological characteristics are the number of nodes per subnetwork, the hop count, the neighbor node density, the hidden nodes, the number of nodes in the neighborhood of the gateway, and the hidden nodes in the neighborhood of the gateway. Network performance metrics are throughput, fairness and delay. The data mining Support Vector Machine (SVM) model was used to extract the relationships between the network topology metrics and the network performance metrics based on data results obtained through ns-2 simulation of random networks. The results obtained can be used as a basis to design channel assignment algorithms or to aid the deployment and management of single radio wireless mesh networks.

The Sixth International Symposium on Neural Networks (ISNN 2009)

A new algorithm to diagnose intermittent scan chain fault in scan-based designs is proposed in this paper. An intermittent scan chain fault sometimes is triggered and sometimes is not triggered during scan chain shifting, which makes it very difficult to locate the fault sites. In this paper, we provide answers to three questions: (1) Why intermittent scan chain faults happen? (2) Why diagnosis of this type of faults is necessary? (3) How to diagnose this type of faults? The experimental results presented demonstrate that the proposed diagnosis algorithm is effective for large industrial designs with multiple intermittent scan chain faults.

The exponential growth of distributed artificial intelligence training workloads has created unprecedented challenges in network performance management and optimization. Traditional monitoring approaches fail to adequately address the dynamic and complex communication patterns inherent in distributed AI systems. This paper presents a comprehensive machine learning-based framework for network performance monitoring and prediction specifically designed for distributed AI training environments. Our approach leverages advanced feature engineering techniques to capture multi-dimensional network performance metrics, including latency variations, bandwidth utilization patterns, and communication bottlenecks. We develop and evaluate multiple machine learning models, including gradient boosting machines, neural networks, and ensemble methods, to predict network performance degradation before it impacts training efficiency. Experimental evaluation on real-world distributed training scenarios demonstrates that our framework achieves 94.7% prediction accuracy for network latency spikes and reduces training time by 23.4% through proactive performance optimization. The proposed monitoring architecture provides real-time insights into network behavior patterns, enabling adaptive resource allocation and communication scheduling optimization. Our contributions include a novel feature extraction methodology, a comprehensive performance prediction model, and a scalable monitoring framework that can be deployed across heterogeneous distributed computing environments.