Moamar Sayed-Mouchaweh

Advanced Pattern Recognition Approach for Fault Diagnosis of Wind Turbines

The production of maximum amount of electrical power from wind requires the improvement of wind turbine reliability. The life duration and the good functioning of the wind turbine depend heavily on the reliability of its blades. Thus, a critical task is to detect and isolate faults, as fast as possible, and regain optimal functioning in the shortest time. In this paper, a pattern recognition approach is proposed for fault diagnosis of a wind turbine, in particular the pitch system composed of actuators and sensors. To achieve this task, feature and decision spaces have been defined. The aim of the pitch system is to adjust the pitch angle of a blade in order to optimize the generated electrical power according to the wind speed. Thus, a fault in the pitch system can reduce the wind turbine performance. Pitch system fault diagnosis is a challenging task because the pitch system feedback compensates the effect of the fault in the pitch actuator. In addition, the observation of the pitch actuator performance is very hard due to the strong variability of the wind speed. A wind turbine simulator is used to validate the performance of the proposed approach.

Decentralized Fault Free Model Approach for Fault Detection and Isolation of Discrete Event Systems

This paper presents a Boolean discrete event model based approach for Fault Detection and Isolation (FDI) of manufacturing systems. This approach considers a system as a set of independent components composed of discrete actuators and their associated discrete sensors. Each component model is only aware of its local desired fault free behavior. The occurrence of a fault entailing the violation of the desired behavior is detected and the potential responsible candidates are isolated using event sequences, time delays between correlated events and state conditions, characterized by sensor readings and control signals issued by the controller. The proposed approach is applied to a flexible manufacturing system.

Inland navigation channel model: Application to the Cuinchy-Fontinettes reach

Inland navigation networks offer an alternative to land transport with economic and environmental benefits, and direct access to urban and industrial centers. Promoting the navigation transport requires the modernization of the network management, in particular the control of navigation levels and the improvement of the network safety. To reach these aims, modeling methods of the inland navigation networks have to be proposed. These networks are large scale distributed systems characterized by non-linearities, time-delays and generally no significant slope. A modeling method of a navigation channel based on identification method is proposed in this paper. It is applied on the Cuinchy-Fontinettes reach located in the north of France.

Drift detection and monitoring in non-stationary environments

Detecting changes in data streams is an important area of research in many applications. The challenging issue is to know how to monitor, update and diagnose these changes so that the accuracy of the learner will be improved whatever the nature of the encountered drifts. In this paper a new error distance based approach for drift detection and monitoring, namely EDIST, is proposed. In EDIST, a difference in error distance distributions of two data-windows is monitored through a statistical hypothesis test. The proposed approach is tested using synthetic data and well known real world data sets. Encouraging results were found comparing to others similar approaches. EDIST has reached the best accuracies in most cases and shown more robustness to noise and false alarms.

Condition monitoring architecture for maintenance of dynamical systems with unknown failure modes

Abstract In this paper, a condition monitoring architecture for maintenance of dynamical systems with unknown failure modes is proposed. In many real applications, dysfunctional analysis techniques do not allow the determination of the complete list of failures that may impact a system. Our proposed architecture allows us to update this a priori analysis. The considered faults are slowly evolving gradual faults known also as drift. The architecture is based on dynamical clustering algorithm which leads to the detection and characterization of drifts amongst the normal operating mode and failure operating modes. The method is highlighted on a case study of a tank system.

A clustering-based approach for the identification of a class of temporally switched linear systems

The behaviours of hybrid dynamic systems (HDS) are determined by combining continuous variables with discrete switching logic. The identification of a HDS aims to find an accurate model of the systems dynamics based on its past inputs and outputs. In pattern recognition (PR) methods, each mode is represented by a set of similar patterns that form restricted regions in the feature space. These sets of patterns are called classes. A pattern is a vector built from past inputs and outputs. HDS identification is a challenging problem since it involves the estimation of different sets of parameters without knowing in advance which sections of the measured data correspond to the different modes of the system. Therefore, HDS identification can be achieved by combining two steps: clustering and parameter estimation. In the clustering step, the number of discrete modes (i.e., the classes that input-output data points belong) is estimated. The parameter estimation step finds the parameters of the models that govern the continuous dynamics in each mode. In this paper, an unsupervised PR method is proposed to achieve the clustering step of the identification of temporally switched linear HDS. The determination of the number of modes does not require prior information about the modes or their number.

Drift detection and characterization for fault diagnosis and prognosis of dynamical systems

In this paper, we present a methodology for drift detection and characterization. Our methodology is based on extracting indicators that reflect the health state of a system. It is situated in an architecture of fault diagnosis/prognosis of dynamical system that we present in this paper. A dynamical clustering algorithm is used as a major tool. The feature vectors are clustered and then the parameters of these clusters are updated as each feature vector arrives. The cluster parameters serve to compute indicators for drift detection and characterization. Then, a prognosis block uses these drift indicators to estimate the remaining useful life. The architecture is tested on a case study of a tank system with different scenarios of single and multiple faults, and with different dynamics of drift.

Abrupt and Drift-Like Fault Diagnosis of Concurent Discrete Event Systems

Discrete Event Systems (DES) are dynamical systems that evolve according to the asynchronous occurrence of certain changes called events. This paper proposes a modular approach for abrupt and drift-like fault diagnosis of concurrent DES. In this class of DES, the system consists of several components or subsystems that operate concurrently. Each component is modeled as a sequence of predetermined actions as well as the responses to these actions. Each component model represents the desired (nominal) system behavior. An abrupt fault is viewed as a violation of the component desired behavior. While a drift-like fault is viewed as a drift in the normal characteristics of component response to actions. An indicator measuring the change in the response characteristics of the component is used to detect a drift. This detection can be then used to warn a human operator when the component behavior starts to deviate from its normal behavior. The proposed approach is illustrated using a manufacturing system.

A Review on Machine Learning and Data Mining Techniques for Residential Energy Smart Management

In this paper, the different machine learning and data mining approaches used for Residential Energy Smart Management (RESM) will be discussed and classified according to some meaningful criteria. The proposed classification is an attempt to highlight the advantages and limitations of each category. Moreover, we emphasize the complementarity between approaches belonging to different categories and we point out the main challenges that still face RESM.

Graphical Model Based Approach for Fault Diagnosis of Wind Turbines

Wind turbine operation and maintenance costs depend on the reliability of its components. Thus, a critical task is to detect and isolate faults, as fast as possible, and restore optimal operating conditions in the shortest time. In this paper, a machine learning of graphical models approach is proposed for fault diagnosis of wind turbines, in particular pitch system. The role of the latter is to adjust the blade pitch angle by rotating it according to the current wind speed in order to optimize the wind turbine power production. This is achieved by a controller based on blade pitch angles measured by two redundant sensors in each blade. Without the sensor accuracy reading, the controller can be misled and fail to achieve the optimal control strategy according to the current operation conditions. In addition, pitch angle sensors complete failure can lead to dangerous actions of the controller, while fixed or drifted bias of sensor measurements may decrease the controllers efficiency. To better control and overcome these challenges, we propose a methodology that is based on Gaussian acyclic graphical models and the lasso estimate. The methodology has shown the ability to model, and diagnose faults that occur in the pitch system in wind turbines during its normal run and could lead to a fast recovery to the optimal operating conditions.