Fault diagnosis for heat pumps with parameter identification and clustering

For reducing the energy consumption of heat pumps, fault detection and diagnosis (FDD) is fundamental. The FDD system presented is based on a gray-box process model, the parameters of which are identified online. The faults are classified from the parameters using clustering methods. Known clustering techniques have been simplified and new “vector clustering” techniques have been developed for classifying gradual faults. The FDD system has been tested in various real applications, for one of which the results are presented in this work. The contribution lies on the application side with a software tool developed for the fully automated training process.     

A neural network-based procedure for the monitoring of exponential mean

Control charts are widely used for both manufacturing and service industries. Cumulative sum (CUSUM) charts are known to be very sensitive in detecting small shifts in the mean. In this paper, we propose a neural network as an alternative approach to CUSUM charts when monitoring exponential mean. The performance of neural network was evaluated by estimating the average run lengths (ARLs) using simulation. The results obtained with simulated data suggest that control scheme based on neural network is significantly more sensitive to process shifts than CUSUM charts. This research also examines the feasibility of using CUSUM chart and neural network together in detecting process mean shifts. The results indicate that using the two methods in combination is more effective than using the methods separately.     

Fuzzy model-based fault detection and diagnosis for a pilot heat exchanger

This article addresses the design and real-time implementation of a fuzzy model-based fault detection and diagnosis (FDD) system for a pilot co-current heat exchanger. The design method is based on a three-step procedure which involves the identification of data-driven fuzzy rule-based models, the design of a fuzzy residual generator and the evaluation of the residuals for fault diagnosis using statistical tests. The fuzzy FDD mechanism has been implemented and validated on the real co-current heat exchanger, and has been proven to be efficient in detecting and isolating process, sensor and actuator faults.     

Delay-dependent fault detection and diagnosis using B-spline neural networks and nonlinear filters for time-delay stochastic systems

A new fault detection and diagnosis (FDD) scheme is studied in this paper for the continuous-time stochastic dynamic systems with time delays, where the available information for the FDD is the input and the measured output probability density functions (PDFs) of the system. The square-root B-spline neural networks is used to formulate the output PDFs with the dynamic weightings. As a result, the concerned FDD problem can be transformed into a robust FDD problem subjected to a continuous time uncertain nonlinear system with time delays. Delay-dependent criteria to detect and diagnose the system fault are provided by using linear matrix inequality (LMI) techniques. It is shown that this new criterion can provide higher sensitivity performance than the existing result. Simulations are given to demonstrate the efficiency of the proposed approach.     

LPV model-based robust diagnosis of flight actuator faults

A linear parameter-varying (LPV) model-based synthesis, tuning and assessment methodology is developed and applied for the design of a robust fault detection and diagnosis (FDD) system for several types of flight actuator faults such as jamming, runaway, oscillatory failure, or loss of efficiency. The robust fault detection is achieved by using a synthesis approach based on an accurate approximation of the nonlinear actuator–control surface dynamics via an LPV model and an optimal tuning of the free parameters of the FDD system using multi-objective optimization techniques. Real-time signal processing is employed for identification of different fault types. The assessment of the FDD system robustness has been performed using both standard Monte-Carlo methods as well as advanced worst-case search based optimization-driven robustness analysis. A supplementary industrial validation performed on the AIRBUS actuator test bench for the monitoring of jamming, confirmed the satisfactory performance of the FDD system in a true industrial setting.     

Integration of fault diagnosis and control based on a trade-off between fault detectability and closed loop performance

This paper presents a novel methodology for simultaneous optimal tuning of a fault detection and diagnosis (FDD) algorithm and a feedback controller for a chemical plant in the presence of stochastic parametric faults. The key idea is to propagate the effect of time invariant stochastic uncertainties onto the measured variables by using a Generalized Polynomial Chaos (gPC) expansion and the nonlinear first principles’ model of the process. A bi-level optimization is proposed for achieving a trade-off between the fault detectability and the closed loop process variability. The goal of the outer level optimization is to seek a trade-off between the efficiency of detecting a fault and the closed loop performance, while the inner level optimization is designed to optimally calibrate the FDD algorithm. The proposed method is illustrated by a continuous stirred tank reactor (CSTR) system with a fault consisting of stochastic and intermittent variations in the inlet concentration. Beyond achieving improved trade-offs between fault detectability and control, it is shown that the computational cost of the gPC model based method is lower than the Monte Carlo type sampling based approaches, thus demonstrating the potential of the gPC method for dealing with large problems and real-time applications.     

Artificial neural network approach for fault detection in rotary system

The detection and diagnosis of faults in technical systems are of great practical significance and paramount importance for the safe operation of the plant. An early detection of faults may help to avoid product deterioration, performance degradation, major damage to the machinery itself and damage to human health or even loss of lives. The centrifugal pumping rotary system is considered for this research. This paper presents the development of artificial neural network-based model for the fault detection of centrifugal pumping system. The fault detection model is developed by using two different artificial neural network approaches, namely feed forward network with back propagation algorithm and binary adaptive resonance network (ART1). The training and testing data required are developed for the neural network model that were generated at different operating conditions, including fault condition of the system by real-time simulation through experimental model. The performance of the developed back propagation and ART1 model were tested for a total of seven categories of faults in the centrifugal pumping system. The results are compared and the conclusions are presented.     

Fault detection and diagnosis of an industrial steam turbine using a distributed configuration of adaptive neuro-fuzzy inference systems

The issue of fault detection and diagnosis (FDD) has gained widespread industrial interest in process condition monitoring applications. An innovative data-driven FDD methodology has been presented in this paper on the basis of a distributed configuration of three adaptive neuro-fuzzy inference system (ANFIS) classifiers for an industrial 440 MW power plant steam turbine with once-through Benson type boiler. Each ANFIS classifier has been developed for a dedicated category of four steam turbine faults. A preliminary set of conceptual and experimental studies has been conducted to realize such fault categorization scheme. A proper selection of four measured variables has been configured to feed each ANFIS classifier with the most influential diagnostic information. This consequently leads to a simple distributed FDD system, facilitating the training and testing phases and yet prevents operational deficiency due to possible cross-correlated measured data effects. A diverse set of test scenarios has been carried out to illustrate the successful diagnostic performances of the proposed FDD system against 12 major faults under challenging noise corrupted measurements and data deformation corresponding to a specific fault time history pattern.     

BP神经网络在飞控系统传感器故障诊断中的应用

故障检测和诊断技术对提高系统可靠性具有重要意义,针对飞控系统中常见的传感器故障,提出了基于神经网络观测器的故障诊断方法;通过构造神经网络模型代替解析系统建模,利用神经网络的学习能力在线检测传感器故障,最后,应用BP神经网络算法对故障进行仿真;仿真结果表明,神经网络观测器方法对单一传感器故障及多个传感器故障均能够准确识别,并对故障的定位也有不错的效果。     

Vibration signal analysis for electrical fault detection of induction machine using neural networks

Fault detection is desirable for increasing machinery availability, reducing consequential damage, and improving operational efficiency. Many of these faulty situations in three-phase induction motors originate from an electrical source. Vibration signal analysis is found to be sensitive to electrical faults. However, conventional methods require detailed information on motor design characteristics and cannot be applied effectively to vibration diagnosis because of their nonadaptability and the random nature of the vibration signals. This paper presents the development of an online electrical fault detection system that uses neural network modeling of induction motor in vibration spectra. The short-time Fourier transform is used to process the quasi-steady vibration signals for continuous spectra so that the neural network model can be trained. The electrical faults are detected from changes in the expectation of modeling errors. Experimental observations show that a robust and automatic electrical fault detection system is produced whose effectiveness is demonstrated while minimizing the triggering of false alarms due to power supply imbalance.     

一类带有传染器故障的混合系统的容错控制

A model-based fault tolerant control approach for hybrid linear dynamic systems is proposed in this paper. The proposed method, taking advantage of reliable control, can maintain the performance of the faulty system during the time delay of fault detection and diagnosis (FDD) and fault accommodation (FA), which can be regarded as the first line of defence against sensor faults.Simulation results of a three-tank system with sensor fault are given to show the efficiency of the method.     

Fault detection model‐based controller for process systems

This paper develops a model‐based control system for fault detection and controller reconfiguration using stochastic model predictive control (MPC). The system can determine online the optimal control actions, detect faults quickly, and reconfigure the controller accordingly. Such a system can perform its function correctly in the presence of internal faults. A fault detection model based (FDMB) controller consists of two main parts: the first is fault detection and diagnosis (FDD) and the second is controller reconfiguration (CR). Systems subject to such abrupt failures are modeled as stochastic hybrid systems with variable‐structure. This paper deals with three challenging issues: design of the fault‐model set; estimation of hybrid multiple models; and stochastic MPC of hybrid multiple models. For the first issue, we propose a simple scheme for designing a fault model set based on random variables. For the second issue, we consider and select a fast and reliable FDD system applied to the above model set. Finally, we develop a stochastic MPC scheme for multiple model CR with soft switching signals based on the weighted probabilities of the outputs of different models. Simulations for the proposed FDMB controller are illustrated and analyzed. Copyright © 2011 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society     

一类带有传感器故障的混合系统的容错控制

A model-based fault tolerant control approach for hybrid linear dynamic systems is proposed in this paper. The proposed method, taking advantage of reliable control, can maintain the performance of the faulty system during the time delay of fault detection and diagnosis (FDD) and fault accommodation (FA), which can be regarded as the first line of defence against sensor faults. Simulation results of a three-tank system with sensor fault are given to show the efficiency of the method.     

基于改进图卷积神经网络的配网线路故障诊断

随着电网的不断扩容,系统结构越来越复杂,多故障频发,而多故障是故障诊断的关键和难点。为解决故障处理数据量大,需要快速、准确地诊断电网故障的问题,本文提出了一种基于模糊优化图卷积神经网络的配网故障诊断模型。首先处理采集的配网故障线路的特征数据;其次,搭建基于图卷积神经网络的故障诊断模型,利用模糊理论建立配电网故障的隶属函数;最后利用训练好的模型进行配网故障诊断。仿真结果表明,模糊优化图卷积神经网络对多故障诊断的准确率高于卷积神经网络以及其他方法,本文方法做出的诊断结果更加精确,综合诊断效果最好。     

Fault detection and diagnosis approach based on nonlinear parity equations and its application to leakages and blockages in the drying section of a board machine

This study aims at providing a fault detection and diagnosis (FDD) approach based on nonlinear parity equations identified from process data. Process knowledge is used to reduce the process nonlinearity from high to low-dimensional nonlinear functions representing common process devices, such as valves, and incorporating the monotonousness properties of the dependencies between the variables. The fault detection approach considers the obtained process model to be nonlinear parity equations, and fault diagnosis is carried out with the standard structured residual method. The applicability of the approach to complex flow networks controlled by valves is tested on the drying section of an industrial board machine, in which the key problems are leakages and blockages of valves and pipes in the steam–water network. Nonlinear model equations based on the mass balance of different parts of the network are identified and validated. Finally, fault detection and diagnosis algorithms are successfully implemented, tested, and reported.     

A study of fault diagnosis in a scooter using adaptive order tracking technique and neural network

An expert system for scooter fault diagnosis using sound emission signals based on adaptive order tracking and neural networks is presented in this paper. The order tracking technique is one of the important approaches for fault diagnosis in rotating machinery. The different faults present different order figures and they can be used to determine the fault in mechanical systems. However, many breakdowns are hard to classify correctly by human experience in fault diagnosis. In the present study, the order tracking problem is treated as a parametric identification and the artificial neural network technique for classifying faults. First, the adaptive order tracking extract the order features as input for neural network in the proposed system. The neural networks are used to develop the training module and testing module. The artificial neural network techniques using a back-propagation network and a radial basis function network are proposed to develop the artificial neural network for fault diagnosis system. The performance of two techniques are evaluated and compared through experimental investigation. The experimental results indicated that the proposed system is effective for fault diagnosis under various engine conditions.     

组合概率信息的复杂动态系统层次故障检诊方法

基于对系统故障的功能性特征的考虑和对子系统模态的精确定义,提出一种构造系统 诊断用的层次模型的方法,并由此提出一种在此模型中层次地推进检诊过程的方法,基本诊断 手段采用de kleer的GDE通用诊断推理机.层次的诊断方式极大地提高了诊断的效率,子系 统模态的适当定义保证了层次间界限分明,消除了诊断算法在不同层次间可能的重复,产生了 简炼的层次间概率传递公式.     

Fault detection in catalytic cracking converter by means of probability density approximation

The paper deals with a model-based fault diagnosis for a catalytic cracking converter process realized using artificial neural networks. Modelling of the considered process is carried out by using a locally recurrent neural network. Decision making about possible faults is performed using statistical analysis of a residual. A neural network is applied to density shaping of a residual. After that, assuming a significance level, a threshold is calculated. The proposed approach is tested on the example of a catalytic cracking converter at the nominal operating conditions as well as in the case of faults.     

A fuzzy neural network approach for online fault detection in waste water treatment process

In this paper, an effective strategy for fault detection of sludge volume index (SVI) sensor is proposed and tested on an experimental hardware setup in waste water treatment process (WWTP). The main objective of this fault detection strategy is to design a system which consists of the online sensors, the SVI predicting plant and fault diagnosis method. The SVI predicting plant is designed utilizing a fuzzy neural network (FNN), which is trained by a historical set of data collected during fault-free operation of WWTP. The fault diagnosis method, based on the difference between the measured concentration values and FNN predictions, allows a quick revealing of the faults. Then this proposed fault detection method is applied to a real WWTP and compared with other approaches. Experimental results show that the proposed fault detection strategy can obtain the fault signals of the SVI sensor online.     

A review on fault detection and diagnosis techniques: basics and beyond

Safety and reliability are absolutely important for modern sophisticated systems and technologies. Therefore, malfunction monitoring capabilities are instilled in the system for detection of the incipient faults and anticipation of their impact on the future behavior of the system using fault diagnosis techniques. In particular, state-of-the-art applications rely on the quick and efficient treatment of malfunctions within the equipment/system, resulting in increased production and reduced downtimes. This paper presents developments within Fault Detection and Diagnosis (FDD) methods and reviews of research work in this area. The review presents both traditional model-based and relatively new signal processing-based FDD approaches, with a special consideration paid to artificial intelligence-based FDD methods. Typical steps involved in the design and development of automatic FDD system, including system knowledge representation, data-acquisition and signal processing, fault classification, and maintenance related decision actions, are systematically presented to outline the present status of FDD. Future research trends, challenges and prospective solutions are also highlighted.