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With the continuous development of large-scale, continuous and complex industries process, the reliability and safety requirements are getting higher and higher of the industrial equipment and control system. Process monitoring has become one of the most important research directions in process automation. In this paper, the ICA-based continuous industrial process system monitoring methods were studied...
In this paper, a novel kernel independent component analysis method which is named improved DKICA is proposed for dynamic industry processes' fault detection and fault diagnosis. The primary idea of this method is how to obtain an augmented measurement matrix in the data kernel space, the independent component analysis is used, so the dynamic and nonlinear features can be extracted in non-linear non-Gaussian...
Pneumatic control valves are the most frequently used actuators in industrial processes. Its property definitely affects the performance of processes and therefore process monitoring of the pneumatic control valve is of great importance. Canonical Variate Analysis (CVA) is a multivariate data-driven method which considers time correlations and has been demonstrated to be superior to some methods in...
A new MATLAB toolbox DB-KIT was recently developed for the design and implementation of fault diagnosis systems. For the purpose of key performance indicator (KPI) oriented fault detection, over the past few years, a series of test statistics and the corresponding thresholds were derived based on the modified data structures originating from the existing multivariate statistical analysis tools. These...
In this paper we develop a fault detection and isolation method based on data-driven approach. Data-driven methods are effective for feature extraction and feature analysis using statistical techniques. In the proposal, the Cumulated Sum (CUSUM) efficiency is explored for incipient fault detection. The fault is assumed to be a Gain variation, an Offset evolution, a Phase shifting or one of the multiple...
Fault detection needs to be accurate and precise to make right decisions about the systems operation status, unfortunately, monitoring processes via multivariate statistical control (MSPC) such as principal component analysis (PCA) arises the problem of false alarms. One solution to this problem is to increase the confidence intervals of the monitoring indices thresholds; however, doing that will...
For modern industrial processes, timely detection of incipient faults is of vital importance so as to ensure safe and optimal process operation. Though recently statistical process monitoring (SPM) has been extensively studied and widely applied in practice, conventional multivariate statistics are usually not sensitive to incipient faults. In this paper, a new multivariate statistical index called...
As technology evolves, the Internet of Things (IoT) is gaining more importance for constituting a foundation to reach better connectivity between people and things. For this to happen, certain strategies and processes are considered to enhance and grant optimal interoperability between the heterogenous devices of a typical IoT network. Two major key aspects of these networks are autonomous error recovery...
This paper presents an efficient fault detection approach to monitor the direct current (DC) side of photovoltaic (PV) systems. The key contribution of this work is combining both single diode model (SDM) flexibility and the cumulative sum (CUSUM) chart efficiency to detect incipient faults. In fact, unknown electrical parameters of SDM are firstly identified using an efficient heuristic algorithm,...
It is costly to have a conventional three phase electricity supply system to service sparsely-populated rural areas. As a solution, the single wire earth return (SWER) network is widely used which has only one overhead conductor delivering power and the ground is used for the return path. This paper presents an on-line monitoring system, currently under development, to provide real-time power quality...
Principal component analysis (PCA) is widely used for fault detection for chemical processes; however, the efficient principal component (PC) selection remains an challenge. The effect of PC selection on PCA-based fault detection performance is analyzed within the statistical framework of hypothesis testing. A performance-driven fault-relevant PC (FRPC) subspace construction integrated with Bayesian...
A novel fault detection method based on margin statistics of generalized non-negative matrix factorization (GNMF) is proposed. The construction of traditional process monitoring method based on multivariate statistical that neglects the correlation relation and feature distribution of latent variables at different sampling times, and the method also need to assume that latent variables satisfy a particular...
In this paper, the distributed fault detection (FD) problem is addressed for a class of linear stochastic multi-agent systems (MASs). A novel distributed cooperative control scheme is given for reaching bounded formation of the MAS. It is proved that by using relative outputs between neighboring agents, a set of distributed fault detection filters can be designed for each agent to detect the faults...
Data-driven fault detection technique has been widely applied for process monitoring, which can effectively detect faults happened in industrial processes. It is extremely significant for guaranteeing the normal operation of processes. Independent Component Analysis (ICA), a type of Data-driven fault detection technique, has been successfully applied to Blind Source Separation and process fault detection...
In view of the shortcomings of traditional fault diagnosis system in accuracy, robustness and too complicated recognition algorithm, the distributed intelligent fault diagnosis system is presentation based on ZigBee technology and particle filter. Multi variable data's acquisition and centralized treatment is realized through the wireless sensor network, and the accurate estimation of the state of...
Process monitoring of incipient faults, as opposed to abrupt faults, in an industrial process is increasingly becoming more important. These are slowly developing faults that may eventually lead to severe abnormal conditions, and ultimately, failure of a critical component. Data-driven multivariate statistical process monitoring (MSPM) methods are extensively studied and widely used for abrupt fault...
Process monitoring plays a vital role in order to sustain optimal operation and maintenance of the plant in process industry. As an essential stage in process monitoring, datadriven fault detection and diagnosis techniques have evolved quickly owing to the prosperity of multivariate feature extraction methods. In addition to the application of basic feature extraction methods, hybrid algorithms combining...
Deployed test and health monitoring systems must actually manage two tasks: (1) the testing and monitoring the device under test, and (2) the testing and monitoring the test system itself. The ability to isolate and identify faults is crucial to deployed test and health monitoring systems. Understanding failure mechanisms provides invaluable insight regarding whether the device under test is, in fact,...
In recent years, the use of the internet has become widespread with developing technologies. Internet is used for many needs, especially social media. Today, internet is needed for remote use of electronic devices used in homes and offices. Continuous access to the internet is very important for the quality of life of people. In this study, a proposal was made for early detection of basic faults that...
Elevators are the means that people often use in everyday life. From the past until nowadays many elevators have been used in many areas. Elevator systems with the formation of high-rise buildings in recent years has become more important. Early diagnosis of faults that may occur in the elevator system is very important. In this study, an approach has been proposed to monitor and detect faults on...
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