Adel Haghani

Observer-based FDI Schemes for Wind Turbine Benchmark

Abstract In this paper, observer-based FDI schemes for wind turbines are proposed. This study is based on the benchmark model presented in Odgaard et al. [2009a]. For residual generation, Kalman filter and diagnostic observer based approaches are employed, and for residual evaluation, generalized likelihood ratio test and cumulative variance index are chosen. The fault isolation issue is solved with the help of dual sensor redundancy. Finally, the performance of the proposed FDI schemes is systematically evaluated by Monte Carlo studies.

Fuzzy Adaptive Tracking Control of Constrained Nonlinear Switched Stochastic Pure-Feedback Systems

In this paper, the fuzzy adaptive control problem for a class of switched stochastic nonlinear systems in pure feedback form with output constraint is addressed. By proposing a nonlinear mapping, the constrained system is transformed into an unconstrained one, with equivalent control objective. All signals in the closed-loop system are proved to be semi-globally uniformly ultimately bounded. Meanwhile, the output constraint is satisfied and the output tracking error converges to an arbitrarily small neighborhood of zero. Finally, the applicability of the proposed controller is verified by a simulation example.

Quality-Related Fault Detection in Industrial Multimode Dynamic Processes

Multivariate statistical process monitoring (MSPM) methods are powerful tools for detecting faults in industrial systems. However, industrial processes are often subjected to dynamic changes. This dynamic behavior is mainly due to set-point changes and nonlinearities. Because of the nonlinearity of processes, the performance of the classical MSPM methods, which are mainly based on the linearity assumption, becomes unsatisfactory, since the process characteristics will change from one operating point to another. The main objective of the work is to develop an efficient fault detection technique for complex industrial systems, using process historical data and considering the nonlinear behavior of the process. In the proposed approach, the nonlinear system is assumed to be linear around the operating points and therefore considered as a piecewise linear system corresponding to each operating mode. The performance and effectiveness of this approach are demonstrated using data obtained from a paper machine and compared with an available method.

On PCA-based fault diagnosis techniques

This paper presents the application of standard PCA technique to fault diagnosis system design. Based on the fault detectability analysis of existed test statistics, the joint use of some test statistics is recommended. Our further study is dedicated to develop a fault isolation approach based on likelihood ratio test, on which the off-set and scaling fault can be easily isolated with explicit off-set fault direction and scaling fault classification. The issues of off-set and scaling fault identification will be also discussed and the complete scheme of PCA-based fault diagnosis procedure is proposed.

Data-driven quality monitoring and fault detection for multimode nonlinear processes

This paper addresses the problem of quality monitoring and fault detection in nonlinear processes which are working in different operating points. For such processes the statistical model which is obtained from process data is different from one operating point to another, due to nonlinearities and set-point changes. Therefore the classical methods for quality monitoring and fault detection, e.g. partial least squares (PLS), may not be suitable. To this end, a new approach is proposed based on the modeling of nonlinear process as a piecewise linear parameter varying system, considering the behavior of the plant in each operating point as linear time invariant with different parameters in each operating point. The expectation-maximization (EM) algorithm is used to model the process as a finite mixture of Gaussian components and based on the identified model a Bayesian inference strategy is developed to detect the faults which influence the product quality. Finally, the usefulness of the proposed method is demonstrated on a laboratory continuous stirred tank heater (CSTH) setup.

A data-driven approach for sensor fault diagnosis in gearbox of wind energy conversion system

Due to the increase in worldwide energy demand, wind energy technology has been developed rapidly over the past years. 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 (O&M) costs. The state of the art methodologies for WEC condition monitoring are signal analysis, observer-based approach, neural networks, etc. In this paper, an effective and easy adaptable multivariate data-driven method for wind turbine monitoring and fault diagnosis is introduced, which consists of three parts: 1) off-line training process 2) on-line monitoring phase 3) on-line diagnosis phase. The performance of this method is validated for detection of sensor abnormalities that have occurred in real wind turbines.

An approach for multimode dynamic process monitoring using Bayesian inference

Abstract The problem of data driven design of fault detection system for dynamic processes when the system is subjected to set-point changes as well as model changes due to different operation regimes is studied in this paper. To this end, a new monitoring approach is developed where the finite Gaussian mixture model is firstly identified directly from available historical data. The mixture model contains the mean and covariance information of the data for each component. Then an observer based residual generator is designed by identification of the parity vectors using covariance matrices of the mixture model. A Bayesian approach is further utilized for the purpose of fault detection. The performance and effectiveness of the proposed approach are finally demonstrated on a continuous stirred tank heater (CSTH) benchmark process.

Data-driven design of KPI-related fault-tolerant control system for wind turbines

In this paper, a scheme for an integrated design of fault-tolerant control (FTC) systems for a wind turbine benchmark is proposed, with focus on the overall performance of the system. For that a key performance indicator (KPI) which reflects the economic performance of the system is defined, and the objective of the proposed FTC scheme is to maintain the system KPI in the admissible range in faulty conditions. The basic idea behind this scheme is data-driven design of the proposed fault-tolerant architecture whose core is an observer/residual generator based realization of the Youla parameterization of all stabilizing controllers with an embedded residual generator for fault detection (FD) purpose. The performance and effectiveness of the proposed scheme are demonstrated through the wind turbine benchmark model proposed in [1].

Guidance, Navigation and Control of Unmanned Surface Vehicles

Abstract In this paper, a framework for guidance, navigation and control of marine vehicles is proposed. The focus is on application of unmanned surface vehicle for performing high accuracy measuring tasks. The desired maneuvering path is defined as a combination of standard path sections and delivered to the guidance system. The measurements are filtered and the unmeasurable states are estimated in navigation system and fed back to the guidance and control systems. An extended Kalman filter based approach is used in navigation system, in order to take the nonlinear behaviors of the system into consideration. The proposed control scheme is a modular system consisting of a cascade structure, where the unified inner loop is responsible for velocity control and the outer loop can be adapted independently to various missions. The model parameters are estimated based on subspace identification method using data obtained from a measuring vehicle, MESSIN. The results have been further used for design of control and navigation systems. The performance and effectiveness of the proposed framework are demonstrated on a model for a path following mission.

Data-driven estimation of air mass using Gaussian mixture regression

The modelling and calculation of charge cycles with conventional intake manifold pressure based extensions is difficult to implement in real-time for combustion engines with extra actuators in valve train (VVT - variable valve timing) on current control units. Additionally, there is a high parametrization effort due to a variety of engine characteristics of this approach. In this paper we will analyse a cycle based calculation of the air mass with regard to an applicability for estimation in real time on the engine unit as well as varying options of actuators and sensor equipment components of combustion engines. We present a physical based, zero-dimensional model and the problem of its real-time realization is discussed. Furthermore, we will introduce a data-driven alternative for estimation of air mass using Gaussian Mixture Regression (GMR). The GMR allows a flexible data-driven modelling with a high input space dimensions together with a perspective of possibilities of adaption and local optimisation. Subsequently, the proposed method will be applied to a current Volkswagen (VW) Otto engine and the results discussed.