The recent explosion in different statistics for fault detection has meant that the practitioner is faced with the unenviable job of determining which to use in a given situation. Thus, this paper seeks to investigate the different test statistics that can be applied to detect multiplicative faults for multivariate Gaussian-distributed processes in order to provide the practitioner with some guidance. Three groups of methods are: traditional methods (e.g., T 2 and Q statistics) and their extensions; the Wishart distribution-based methods; and those methods that are created in information and communication fields to describe the characteristics of measurement variance and covariance (e.g., mutual information and Kullback-Leibler divergence). Then, greater details on their interconnections and comparisons are presented and their performance for detecting multiplicative faults is evaluated and demonstrated using numerical simulations.
In this article, an issue of data-driven optimal strictly stealthy attack design for the stochastic linear invariant systems is investigated, with the aim of maximizing the system performance degradation under an energy bounded constraint and bypassing the parity-space-based attack detector. Importantly, the proposed attack policy refrains from the assumption that the system knowledge is known to attackers. A novel strictly stealthy attack sequence (SSAS), coordinating the sensor and actuator signals simultaneously, is proposed with a sufficient and necessary condition for the existence of such an attack presented. Specifically, the SSAS is parameterized as a vector in the null space of a specific matrix which is constructed by a parity matrix and the system Markov parameters. For the purpose of data-driven attack realization, modified subspace identification methods are utilized to achieve an unbiased estimation of the required parameters via the closed-loop data. On this basis, the attack design is formulated as a constrained optimization problem, an explicit solution to which is given to characterize the optimal strictly stealthy attack. Finally, the vulnerability of the cyber-physical systems is analysed from the perspective of the parameter selection for the parity space-based detector. A case study on a three-tank model verifies the efficiency of the proposed approach.
With a fast growing of wind power installed capacity, an efficient monitoring system for wind energy conversion system (WEC) is required to ensure operational reliability, high availability of energy production and at the same time reduce operating and maintenance costs. In this paper, an efficient, scaleable data-driven on-line WEC monitoring method is introduced, which consists of three parts: 1) off-line training process 2) on-line monitoring phase 3) on-line diagnosis phase. The performance and effectiveness of this method for the detection of process abnormalities are demonstrated using the data obtained from different wind turbines in a wind farm.
This paper addresses fault-tolerant control (FTC) issues for linear systems with model uncertainty and multiplicative faults. The left and right coprime factorization techniques are first adopted for system modeling. Then, the fault detection (FD) approaches are investigated in the coprime factorization context. Based on the information provided by the FD systems, the corresponding FTC architectures and design schemes are presented. Moreover, the gap metric techniques are applied to fault detectability analysis, including the fault detectability indicators to quantify the detection performance in the presence of model uncertainty. The effectiveness of the developed methods for industrial application is illustrated by a case study on a dc motor.
The pull-based development is widely adopted in modern open-source software (OSS) projects, where developers propose changes to the codebase by submitting a pull request (PR). However, due to many reasons, PRs in OSS projects frequently experience delays across their lifespan, including prolonged waiting times for the first response. Such delays may significantly impact the efficiency and productivity of the development process, as well as the retention of new contributors as long-term contributors. In this paper, we conduct an exploratory study on the time-to-first-response for PRs by analyzing 111,094 closed PRs from ten popular OSS projects on GitHub. We find that bots frequently generate the first response in a PR, and significant differences exist in the timing of bot-generated versus human-generated first responses. We then perform an empirical study to examine the characteristics of bot- and human-generated first responses, including their relationship with the PR's lifetime. Our results suggest that the presence of bots is an important factor contributing to the time-to-first-response in the pull-based development paradigm, and hence should be separately analyzed from human responses. We also report the characteristics of PRs that are more likely to experience long waiting for the first human-generated response. Our findings have practical implications for newcomers to understand the factors contributing to delays in their PRs.
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