Performance Tradeoffs for Networked Jump Observer-Based Fault Diagnosis

Print Request Permissions In this paper, we address the fault diagnosis problem for discrete-time multi-sensor systems over communication networks with measurement dropouts. We use the measurement outcomes to model the measurement reception scenarios. Based on this, we propose the use of a jump observer to diagnose multiple faults. We model the faults as slow time-varying signals and introduce this dynamic in the observer to estimate the faults and to generate a residual. The fault detection is assured by comparing the residual signal with a prescribed threshold. We design the jump observer, the residual and the threshold to attain disturbance attenuation, fault tracking and detection conditions and a given false alarm rate. The false alarm rate is upper bounded by means of Markov's inequality. We explore the tradeoffs between the minimum detectable faults, the false alarm rate and the response time to faults of the fault diagnoser. By imposing the disturbances and measurement noises to be Gaussian, we tighten the false alarm rate bound which improves the time needed to detect a fault. A numerical example is provided to illustrate the effectiveness of the theory developed in the paper

Reinforcement Learning for Fuzzing Testing Techniques

Generally, the present disclosure is directed to using machine learning to manage a trade-off between exploration and exploitation in a fuzzer system. In particular, in some implementations, the systems and methods of the present disclosure can include or otherwise leverage one or more machine-learned models to predict inputs that will cause a system being tested to malfunction or crash based on input data provided to the system and output from the system

False data injection attack detection in smart grid

Smart grid is a distributed and autonomous energy delivery infrastructure that constantly monitors the operational state of its overall network using smart techniques and state estimation. State estimation is a powerful technique that is used to determine the overall operational state of the system based on a limited set of measurements collected through metering systems. Cyber-attacks pose serious risks to a smart grid state estimation that can cause disruptions and power outages resulting in huge economical losses and are therefore a big concern to a reliable national grid operation. False data injection attacks (FDIAs), engineered on the basis of the knowledge of the network configuration, are difficult to detect using the traditional data detection mechanisms. These detection schemes have been found vulnerable and failed to detect these FDIAs. FDIAs specifically target the state data and can manipulate the state measurements in such a way that these false measurements appear real to the main control systems. This research work explores the possibility of FDIA detection using state estimation in a distributed and partitioned smart grid. In order to detect FDIAs we use measurements for residual-based testing which creates an objective function; and the probability of erroneous data is determined from this residual test. In this test, a preset threshold is determined based on the prior history of the state data. FDIA cases are simulated within a smart grid considering that the Chi-square detection state estimator fails in identifying such attacks. We compute the objective function using the standard weighted least problem and then test the objective function against the value in the Chi-square table. The gain matrix and the Jacobian matrix are computed. The state variables are computed in the form of a voltage magnitude. The state variables are computed after the inception of an attack to assess these state magnitude results. Different sizes of partitioning are used to improve the overall sensitivity of the Chi-square results. Our additional estimator is based on a Kalman estimation that consists of the state prediction and state correction steps. In the first step, it obtains the state and matrix covariance prediction, and in the second step, it calculates the Kalman gain and the state and matrix covariance update steps. The set of points is created for the state vector x at a time instant t. The initial vector and covariance matrix are based on a priori knowledge of the historical estimates. A set of sigma points is estimated by the state update function. Sigma points refer to the minimal set of sampling points that are selected and transformed using nonlinear function, and the new mean and the covariance are formed out of these transformed points. The idea behind this is that it is easier to compute a Gaussian distribution than an arbitrary nonlinear function. The filter gain, the mean and the covariance are used to estimate the next state. Our simulation results show that the combination of Kalman estimation and distributed state estimation improves the overall stability index and vulnerability assessment score of the smart grid. We built a stability index table for a smart grid based on the state estimates value after the inception of an FDIA. The vulnerability assessment score of the smart grid is based on common vulnerability scoring system (CVSS) and state estimates under the influence of an FDIA. The simulations are conducted in the MATPOWER program and different electrical bus systems such as IEEE 14, 30, 39, 118 and 300 are tested. All the contributions have been published in reputable journals and conferences.Doctor of Philosoph

On the relevance of preprocessing in predictive maintenance for dynamic systems

The complexity involved in the process of real-time data-driven monitoring dynamic systems for predicted maintenance is usually huge. With more or less in-depth any data-driven approach is sensitive to data preprocessing, understood as any data treatment prior to the application of the monitoring model, being sometimes crucial for the final development of the employed monitoring technique. The aim of this work is to quantify the sensitiveness of data-driven predictive maintenance models in dynamic systems in an exhaustive way. We consider a couple of predictive maintenance scenarios, each of them defined by some public available data. For each scenario, we consider its properties and apply several techniques for each of the successive preprocessing steps, e.g. data cleaning, missing values treatment, outlier detection, feature selection, or imbalance compensation. The pretreatment configurations, i.e. sequential combinations of techniques from different preprocessing steps, are considered together with different monitoring approaches, in order to determine the relevance of data preprocessing for predictive maintenance in dynamical systems

Multivariate Statistical Process Control Charts: An Overview

In this paper we discuss the basic procedures for the implementation of multivariate statistical process control via control charting. Furthermore, we review multivariate extensions for all kinds of univariate control charts, such as multivariate Shewhart-type control charts, multivariate CUSUM control charts and multivariate EWMA control charts. In addition, we review unique procedures for the construction of multivariate control charts, based on multivariate statistical techniques such as principal components analysis (PCA) and partial lest squares (PLS). Finally, we describe the most significant methods for the interpretation of an out-of-control signal.quality control, process control, multivariate statistical process control, Hotelling's T-square, CUSUM, EWMA, PCA, PLS

Autonomous and Resilient Management of All-Source Sensors for Navigation Assurance

All-source navigation has become increasingly relevant over the past decade with the development of viable alternative sensor technologies. However, as the number and type of sensors informing a system increases, so does the probability of corrupting the system with sensor modeling errors, signal interference, and undetected faults. Though the latter of these has been extensively researched, the majority of existing approaches have constrained faults to biases, and designed algorithms centered around the assumption of simultaneously redundant, synchronous sensors with valid measurement models, none of which are guaranteed for all-source systems. This research aims to provide all-source multi-sensor resiliency, assurance, and integrity through an autonomous sensor management framework. The proposed framework dynamically places each sensor in an all-source system into one of four modes: monitoring, validation, calibration, and remodeling. Each mode contains specific and novel realtime processes that affect how a navigation system responds to sensor measurements. The monitoring mode is driven by a novel sensor-agnostic fault detection, exclusion, and integrity monitoring method that minimizes the assumptions on the fault type, all-source sensor composition, and the number of faulty sensors. The validation mode provides a novel method for the online validation of sensors which have questionable sensor models, in a fault-agnostic and sensor-agnostic manner, and without compromising the ongoing navigation solution in the process. The remaining two modes, calibration and remodeling, generalize and integrate online calibration and model identification processes to provide autonomous and dynamic estimation of candidate model functions and their parameters, which when paired with the monitoring and validation processes, directly enable resilient, self-correcting, plug-and-play open architecture navigation systems

Vibration Monitoring: Gearbox identification and faults detection

L'abstract è presente nell'allegato / the abstract is in the attachmen

Prognostic-based Life Extension Methodology with Application to Power Generation Systems

Practicable life extension of engineering systems would be a remarkable application of prognostics. This research proposes a framework for prognostic-base life extension. This research investigates the use of prognostic data to mobilize the potential residual life. The obstacles in performing life extension include: lack of knowledge, lack of tools, lack of data, and lack of time. This research primarily considers using the acoustic emission (AE) technology for quick-response diagnostic. To be specific, an important feature of AE data was statistically modeled to provide quick, robust and intuitive diagnostic capability. The proposed model was successful to detect the out of control situation when the data of faulty bearing was applied. This research also highlights the importance of self-healing materials. One main component of the proposed life extension framework is the trend analysis module. This module analyzes the pattern of the time-ordered degradation measures. The trend analysis is helpful not only for early fault detection but also to track the improvement in the degradation rate. This research considered trend analysis methods for the prognostic parameters, degradation waveform and multivariate data. In this respect, graphical methods was found appropriate for trend detection of signal features. Hilbert Huang Transform was applied to analyze the trends in waveforms. For multivariate data, it was realized that PCA is able to indicate the trends in the data if accompanied by proper data processing. In addition, two algorithms are introduced to address non-monotonic trends. It seems, both algorithms have the potential to treat the non-monotonicity in degradation data. Although considerable research has been devoted to developing prognostics algorithms, rather less attention has been paid to post-prognostic issues such as maintenance decision making. A multi-objective optimization model is presented for a power generation unit. This model proves the ability of prognostic models to balance between power generation and life extension. In this research, the confronting objective functions were defined as maximizing profit and maximizing service life. The decision variables include the shaft speed and duration of maintenance actions. The results of the optimization models showed clearly that maximizing the service life requires lower shaft speed and longer maintenance time