To ensure a constant and satisfactory product quality, close monitoring of batch processes is an absolute requirement in the chemical and biochemical industry. Principal Component Analysis (PCA)-based techniques exploit historical databases for fault detection and diagnosis of the current batch run. To handle the dynamic nature of batch processes, dedicated techniques such as Batch Dynamic PCA ...

This paper summarizes basic concepts and the current state of the art in spacecraft fault detection, isolation and recovery. A gap analysis focuses on drawbacks of classical fault diagnosis methods in deep space applications. Studies that propose innovative techniques are summarized and evaluated briefly with respect to enhanced spacecraft on-board fault diagnosis on system level. Research chal...

Current research in the field of robotics deals with planning and executing of increasingly complex tasks as well as applications in which autonomous mobile robot systems interact with highly dynamic environments. In order to operate successfully in a highly dynamic physical world a mobile robot needs awareness to adapt its behavior according to the environment. This awareness also has to cope ...

The w orkpresents some simulation results concerning the application of robust model{based fault diagnosis to an industrial process by using iden ti cation and disturbance de{coupling techniques. The rst step of the considered approach iden ti es sev eral equation error models b y means of the input{output data acquired from the monitored system. Each model describes the di erent w orkingcondit...

When testing from Finite State Machines (FSMs), a failure observed in the Implementation Under Test (IUT) is called a symptom. A symptom could have been caused by an earlier state transfer failure. Transitions that may be used to explain the observed symptoms are called diagnosing candidates. Finding strategies to generate an optimal set of diagnosing candidates that could effectively identify ...

This paper presents a fault detection and isolation scheme for multirate systems with a fast input sampling rate and slower output sampling rates. We design a separate observer for each set of simultaneous measurements with the observer operating at their sampling rate. We use an unknown input observer design to allow state estimation in the presence of disturbances and modelling errors. The ob...

In some multivariate statistical control applications, the data of the process cannot be precise and defined linguistically in practice. Using multivariate control charts in such situations with non-precise data leads to misleading results. In this paper, a new neural network-based monitoring scheme is presented by considering fuzzy multivariate multinomial data. The proposed approach is also a...

As a result from the demanding of process safety, reliability and environmental constraints, a called of fault detection and diagnosis system become more and more important. This statement can be seen from literature covering different aspects and applications implementation. A variety of methods already proposed whether applied single or combined method. One of the feasible ways is by combinin...

This paper develops a fault diagnosis methodology for civil engineering structures based on the bond graph approach. The bond graph theory provides a modeling framework that includes parametric models of the physical system and the sensors. Structural faults are modeled as abrupt or gradual damage in structural components. Sensor faults are modeled as biases or drifts from true measurements. Fa...

Due to the increasing complexity of modern technical processes, the most critical issues in the design of an automated system nowadays are safety/reliability, higher performance and cost efficiency. Faults in process components can lead to a considerable reduce of the efficiency of the process, quality of the product and in some cases even result in fatalities. In order to avert these losses, a...