In this paper it is investigated, how the energy monitoring of robotic systems can be applied for detection and isolation of robot actuator faults. Firstly, the relations describing the energy balance of a robot are recalled. Then it is showed how the actuator faults influence the energy balance of the robot. The deviation of the robot energy from its normal balance is used to detect the presence of fault in the robot actuator. For fault isolation the set of fault energy patterns is defined. The fault induced energy deviation is compared with the elements of the fault energy pattern set using standard signal processing methods. The problems of fault identification and multiple fault detection are also treated. Simulation results are provided to show the applicability of the proposed fault detection and isolation method.

 This paper presents the study of the detection and diagnosis of multiple faults in a Gas Turbine using principal component analysis and structured residuals method. The study includes developing a mathematical model and implementation of algorithms for detection and diagnosis of multiple faults in the system using principal component analysis and structured residuals.

 Boilers are considered as the stem of the most of real industries such as power plant generation and petrochemical industry. So, it is essential to improve performance and safety related issues for a safe and efficient operation. A sudden shut down of a boiler unit causes a huge loss of the operating revenue. Hence to enhance availability and reduce the shut down times, the application of fault detection and diagnosis methods will play a great role in early identification of faults and selecting the most suitable fault tolerant scenario without losing the services. Utility boiler in Sidi Kerir Petrochemicals (SIDPEC) plants is selected as the case study of this work. The boiler has multi-loops, the most important loop which is called the master loop identified on line. The identified model is used to detect real abnormal situation that has been carried during the operation using model based fault diagnosis methods. Different fault scenarios are simulated on the identified model in order to validate the fault detection algorithm. Finally the fault detection algorithm is applied on real abnormal behavior to identify.

 This paper deals with wavelet decomposition applied to fluid leak detection and isolation in process engineering. The developed algorithm is sensitive to minor leaks and robust to all the disturbances (hydraulic and thermal perturbations) and transient operating modes. The proposed approach is based on the Discrete Wavelet Transform (DWT) of the sensor mea- surements, the Fast Fourier Transform (FFT) and the properties of the cross-correlation function. Experimental results obtained on different processes show the efficiency of the proposed approach.

 The success of any diagnosis strategy critically depends on the sensors measuring process variables. This paper presents a strategy based on diagnosability maximization for optimally locating sensors in distribution networks. The goal is to characterize and determine the set of sensors that guarantees a maximum degree of diagnosability taking into account a given sensor configuration cardinality constraint. The strategy is based on the structural model of the system under consideration. Structural analysis is a powerful tool for determining diagnosis possibilities and evaluating whether the number and the location of sensors are adequate in order to meet some diagnosis specifications. The proposed approach is successfully applied to leakage detection in a Drinking Water Distribution Network.