Non-linear minimum variance estimation for fault detection systems

A novel model-based algorithm for fault detection in stochastic linear and non-linear systems is proposed. The non-linear minimum variance estimation technique is used to generate a residual signal, which is then used to detect actuator and sensor faults in the system. The main advantage of the approach is the simplicity of the non-linear estimator theory and the straightforward structure of the resulting solution. Simulation examples are presented to illustrate the design procedure and the type of results obtained. The results demonstrate that both actuator and sensor faults can be detected successfully

Feature selection and categorization to design reliable fault detection systems.

International audienceIn this work, we will develop a fault detection system which is identified as a classification task. The classes are the nominal or malfunctioning state. To develop a decision system it is important to select among the data collected by the supervision system, only those carrying relevant information related to the decision task. There are two objectives presented in this paper, the first one is to use data mining techniques to improve fault detection tasks. For this purpose, feature selection algorithms are applied before a classifier to select which measures are needed for a fault detection system. The second objective is to use STRASS (STrong Relevant Algorithm of Subset Selection), which gives a useful feature categorization: strong relevant features, weak relevant and/or redundant ones. This feature categorization permits to design reliable fault detection system. The algorithm is tested on real benchmarks in medical diagnosis and fault detection. Our results indicate that a small number of measures can accomplish and perform the classification task and shown our algorithm ability to detect the correlated features. Furthermore, the proposed feature selection and categorization permits to design reliable and efficient fault detection system

Data-based fault detection in chemical processes: Managing records with operator intervention and uncertain labels

Developing data-driven fault detection systems for chemical plants requires managing uncertain data labels and dynamic attributes due to operator-process interactions. Mislabeled data is a known problem in computer science that has received scarce attention from the process systems community. This work introduces and examines the effects of operator actions in records and labels, and the consequences in the development of detection models. Using a state space model, this work proposes an iterative relabeling scheme for retraining classifiers that continuously refines dynamic attributes and labels. Three case studies are presented: a reactor as a motivating example, flooding in a simulated de-Butanizer column, as a complex case, and foaming in an absorber as an industrial challenge. For the first case, detection accuracy is shown to increase by 14% while operating costs are reduced by 20%. Moreover, regarding the de-Butanizer column, the performance of the proposed strategy is shown to be 10% higher than the filtering strategy. Promising results are finally reported in regard of efficient strategies to deal with the presented problemPeer ReviewedPostprint (author's final draft

A quick fault detection system applied to pitch actuators of wind turbines

The design of fast respond fault detection systems to wind turbines results an important subject and represents a notable challenger too. This paper presents a recent approach on a quick response fault detection system to pitch actuators in controlled wind turbines. The obtained time detection is about 10 seconds. Our scheme was possible by manipulating an adaptive parametric estimation block by varying the time scales among the actuator and the identification process dynamics. Additionally, numerical experiments are realized to support the main contribution.Postprint (published version

A quick fault detection system applied to pitch actuators of wind turbines

The design of fast respond fault detection systems to wind turbines results an important subject and represents a notable challenger too. This paper presents a recent approach on a quick response fault detection system to pitch actuators in controlled wind turbines. The obtained time detection is about 10 seconds. Our scheme was possible by manipulating an adaptive parametric estimation block by varying the time scales among the actuator and the identification process dynamics. Additionally, numerical experiments are realized to support the main contribution.Postprint (published version

Estimating uncertainty when using transient data in steady-state calculations

When using measurement data for monitoring there is often a desire for steady-state analysis. On-line condition monitoring and fault detection systems are typical applications where the traditional way of treating transient data is to remove it using methods that require tuning using thresholds. This paper suggests an alternative approach where the uncertainty estimate in a particular variable is increased in response to the presence of transients and through propagation, varies the uncertainty in the result accordingly. The formulation of the approach is described and applied to two examples from building HVAC systems. The approach is demonstrated to be a pragmatic tool that can be used to increase the robustness of calculations from time series data

Variability of indicators used in motor fault detection based on electrical measurements

Online condition assessment, product quality assurance and improved operational efficiency of engineering systems, such as induction motors, has increased in significance due to the advantages it offers in terms of productivity. Early detection of faults would not only allow for extensive trending but also provide advanced warnings regarding the health of the machinery. The implementation of on-line fault detection systems must not only exhibit high level of detection accuracy, but also discriminate between actual incipient faults and false alarms caused by temporal variations in operating conditions. The objective of this research is to develop the elements of a fault detection system suitable for continuous, on-line condition monitoring and assessment of 3