The focus of this work is on Statistical Process Control (SPC) a manufacturing process based available measurements. Two important applications SPC in industrial settings are fault detection and diagnosis (FDD). In work, deep learning (DL) methodology proposed for FDD. We investigate the application an explainability concept (explainable artificial intelligence (XAI)) to enhance FDD accuracy ne...

This paper addresses the design of an observer-based fault diagnosis scheme, which is applied to some of the sensors and actuators of a wind turbine benchmark model. The methodology is based on a modified sliding mode observer (SMO) that allows accurate reconstruction of multiple sensor or actuator faults occurring simultaneously. The faults are reconstructed using the equivalent output err...

This paper addresses the design of an observer-based fault diagnosis scheme, which is applied to some of the sensors and actuators of a wind turbine benchmark model. The methodology is based on a modified sliding mode observer (SMO) that allows accurate reconstruction of multiple sensor or actuator faults occurring simultaneously. The faults are reconstructed using the equivalent output err...

The faulty operation of Heating Ventilation and Air Conditioning (HVAC) systems in commercial buildings can waste vast amounts of energy, cause unnecessary CO2 emissions and decrease occupant thermal comfort, reducing productivity. We propose a new method of automating Fault Detection and Diagnosis (FDD), based on the modelling of operational faults in HVAC subsystems, using techniques from sta...

a r t i c l e i n f o This paper presents a diagnostic Bayesian network (DBN) for fault detection and diagnosis (FDD) of variable air volume (VAV) terminals. The structure of the DBN illustrates qualitatively the casual relationships between faults and symptoms. The parameters of the DBN describe quantitatively the probabilistic dependences between faults and evidence. The inputs of the DBN are...

This research study focuses on the discussion regarding the development of fault detection in gas metering station using an Artificial Neural Network (ANN). The proposed model of fault detection applies ANN approach in order to provide a good detection method for billing purpose. However, one of the main problems faced by gas metering system is the undiagnosed faulty condition of measurement. M...

This paper presents an novel approach for fault detection and diagnosis (FDD) of sensor as well as process faults for Electro-Hydraulic Actuators (EHA) using a bank of residual generators, each of which employs an Extended Kalman Filter (EKF)-based parameter estimator. In traditional sensor fault detection schemes, actual sensor measurements are compared with measurements reconstructed using st...

This paper encompasses a study on the development of a walking gait for fault tolerant locomotion in unstructured environments. The fault tolerant gait for adaptive locomotion fulfills stability conditions in opposition to a fault (locked joints or sensor failure) event preventing a robot to realize stable locomotion over uneven terrains. To accomplish this feat, a ...

wireless sensor networks (wsns) consist of a large number of sensor nodes which are capable of sensing different environmental phenomena and sending the collected data to the base station or sink. since sensor nodes are made of cheap components and are deployed in remote and uncontrolled environments, they are prone to failure; thus, maintaining a network with its proper functions even when und...