In this paper, we present two novel algorithms to realize a finite dimensional, linear time-invariant state-space model from input-output data. The algorithms have a number of common features. They are classified as one of the subspace model identification schemes, in that a major part of the identification problem consists of calculating specially structured subspaces of spaces defined by the input-output data. This structure is then exploited in the calculation of a realization. Another common feature is their algorithmic organization: an RQ factorization followed by a singular value decomposition and the solution of an overdetermined set (or sets) of equations. The schemes assume that the underlying system has an output-error structure and that a measurable input sequence is available. The latter characteristic indicates that both schemes are versions of the MIMO Output-Error State Space model identification (MOESP) approach. The first algorithm is denoted in particular as the (elementary MOESP scheme)...

Subspace model identification-Part 1. The output-error state-space model identification class of algorithms

Fault diagnosis of rotary machinery components using a stacked denoising autoencoder-based health state identification

In this paper, two online schemes for an integrated design of fault-tolerant control (FTC) systems with application to Tennessee Eastman (TE) benchmark are proposed. Based on the data-driven design of the proposed fault-tolerant architecture whose core is an observer/residual generator based realization of the Youla parameterization of all stabilization controllers, FTC is achieved by an adaptive residual generator for the online identification of the fault diagnosis relevant vectors, and an iterative optimization method for system performance enhancement. The performance and effectiveness of the proposed schemes are demonstrated through the TE benchmark model.

Real-Time Implementation of Fault-Tolerant Control Systems With Performance Optimization

Fault-tolerant control of Markovian jump stochastic systems via the augmented sliding mode observer approach

Survey paper: A survey on industrial applications of fuzzy control

A comparison study of basic data-driven fault diagnosis and process monitoring methods on the benchmark Tennessee Eastman process

Observer-based FDI Schemes for Wind Turbine Benchmark

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.

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

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.

Quality-Related Fault Detection in Industrial Multimode Dynamic Processes

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.

Adel Haghani

Papers

A comparison study of basic data-driven fault diagnosis and process monitoring methods on the benchmark Tennessee Eastman process

Observer-based FDI Schemes for Wind Turbine Benchmark

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

Quality-Related Fault Detection in Industrial Multimode Dynamic Processes

On PCA-based fault diagnosis techniques