Residual-based Fault Detection Research Articles

RAIM and Failure Mode Slope: Effects of Increased Number of Measurements and Number of Faults

This article provides a comprehensive analysis of the impact of the increasing number of measurements and the possible increase in the number of faults in multi-constellation Global Navigation Satellite System (GNSS) Receiver Autonomous Integrity Monitoring (RAIM). Residual-based fault detection and integrity monitoring techniques are ubiquitous in linear over-determined sensing systems. An important application is RAIM, as used in multi-constellation GNSS-based positioning. This is a field in which the number of measurements, m, available per epoch is rapidly increasing due to new satellite systems and modernization. Spoofing, multipath, and non-line of sight signals could potentially affect a large number of these signals. This article fully characterizes the impact of measurement faults on the estimation (i.e., position) error, the residual, and their ratio (i.e., the failure mode slope) by analyzing the range space of the measurement matrix and its orthogonal complement. For any fault scenario affecting h measurements, the eigenvalue problem that defines the worst-case fault is expressed and analyzed in terms of these orthogonal subspaces, which enables further analysis. For h>(m−n), where n is the number of estimated variables, it is known that there always exist faults that are undetectable from the residual vector, yielding an infinite value for the failure mode slope. This article uses the range space and its complement to explain: (1) why, for fixed h and n, the failure mode slope decreases with m; (2) why, for a fixed n and m, the failure mode slope increases toward infinity as h increases; (3) why a failure mode slope can become infinite for h≤(m−n). A set of examples demonstrate the results of the paper.

Residual-Based Fault Detection and Exclusion With Enhanced Localization Integrity

In this paper, we give an integrity framework for vehicle localization in urban environments based on fusion of GPS and dead reckoning. To guarantee localization integrity, a novel Kalman filter-based fault detection and exclusion method is presented to reject faulty measurements via a chi-square test with iterative residuals. For a tight upper-bound for position error, we introduce a protection level calculation method with minimum detectable bias and the integrity budget allocation among all failure modes have been improved to enhance the accuracy of positioning. The algorithm provides integrity assessment and reliable estimations of vehicle position information in an iterative manner. Numerical simulations and experiments show that the proposed method can effectively deal with and recover from abrupt faults with a low rate of false alarm and a tight integrity risk bound.

Detection and Diagnosis of Multiple-Dependent Faults (MDFDD) of Water-Cooled Centrifugal Chillers Using Grey-Box Model-Based Method

This paper presents the development and use of benchmarking grey-box models for the detection and diagnosis of multiple-dependent faults (MDFDD) of a water-cooled centrifugal chiller. Models are developed using data recorded by a Building Automation System (BAS) from a central cooling plant of an institutional building. The forward residual-based fault detection model identifies a fault symptom, when the difference between the measured value of target variable and benchmarking value exceeds the corresponding threshold. For the fault diagnosis, most publications start from a known single fault and establish the impact on following variables in the system. This paper presents a rule-based backward approach. The proposed method identifies if (i) the fault symptom is correct (i.e., a variable has abnormal values), or (ii) the fault symptom is incorrect (i.e., the symptom of target variable is caused by impacts generated by other faulty variables due to the dependency between variables), or (iii) both target and regressor variables are abnormal. For testing the proposed MDFDD model, some artificial faults are inserted into the measurement data file, and results are discussed about the method potential for the application.

Residual-Based Fault Detection and Isolation in Lvdc Microgrid

Residual-Based Fault Detection and Isolation in Lvdc Microgrid

Residual based Fault Detection Isolation and Recovery of Greenhouse

Residual based Fault Detection Isolation and Recovery of Greenhouse

Residual-based fault detection isolation and recovery of a greenhouse

Residual-based fault detection isolation and recovery of a greenhouse

Residual based fault detection and exclusion methods applied to Ultra-Wideband navigation

Global Navigation Satellite System (GNSS) has become the main technology in terms of navigation technologies, as it ensures a worldwide absolute outdoor positioning. The transportation sector employs this technology to obtain a position, velocity and time solution for the corresponding outdoor application. When talking about indoor positioning, nevertheless, GNSS becomes an unreliable navigation technology, as the below-noise signals get obstructed. In these cases, the Ultra-Wideband (UWB) technology can be used as a navigation solution, as its anchor trilateration based radiofrequency positioning resembles GNSS’s principle and, depending on the anchor location, it can be used for indoor positioning. However, just like other radiofrequency based technologies, UWB is vulnerable to interferences and the multipath effect. With the aim of overcoming these drawbacks, this article discusses how to apply Fault Detection and Exclusion (FDE) techniques to avoid using faulty anchors when employing UWB in indoor/urban environments such as tunnels or train stations.

Fault detection and isolation in batteries power electronics and chargers

This paper focuses on the residual-based fault detection and isolation (FDI) in batteries power electronics and chargers. Currently, isolation of multiple faults is performed by generating a bank of residuals, one residual signal for each fault. It is shown that, with a combination of different residual evaluation functions, various battery and electronics faults, including battery short/open circuit, sensor biases, input voltage drop, and switches short/open circuit, can be detected and isolated by using the magnitude and slope of a residual signal or its norm that is generated from the battery voltage. In this paper, the constant-current constant-voltage (CCCV) battery chargers based on buck and boost converters are studied. The FDI systems with observer- and historical-based residual generation methods are designed and simulation results are discussed.

A Fault Detection and Isolation Software Framework for Repeatable and Comparable Experimentation

There is an extensive literature available about condition monitoring relying on multi-dimensional data-driven system models and mappings, including proposal of new methods and algorithms,comparison of state-of-the-art methods, and stateof- the-art revisions. But, when practitioners start to implement their own software to carry out their research, there is a lack of articles in the literature with detailed documentation about how to design a framework for repeatable and comparable experimentation. We propose a design for repeatable and comparable experimentation on the field of Data-Driven Residual-Based Fault Detection and Isolation. The framework has already been used for several experiments, with successful results, eliciting features such as (i) decreasing of developing times, (ii) facilitating of configuration management, and (iii) facilitating of collection and comparison of results.

Fault detection and isolation for nonlinear non-affine uncertain systems via sliding-mode techniques

ABSTRACTThis paper considers the fault detection and isolation problem for nonlinear uncertain non-affine systems. The proposed approach is based on an output-feedback stabilisation strategy, which exploits a Luenberger-like nonlinear observer. As a main result, a residual-based fault detection and isolation approach is developed, which relies on the measurable output, some of its derivatives, which are provided exactly by a uniform high-order sliding-mode differentiator, and the observer’s state. The proposed methodology is able to detect some possible components and actuators faults, and to isolate, under some mild conditions, the actuator faults from those ones in components. Simulation results illustrate the feasibility of the proposed approach.

ToMFIR-based incipient fault detection and estimation for high-speed rail vehicle suspension system

High requirements on safety and reliability of the high-speed railway vehicle suspension (HRVS) system demand accurate detection and estimation of various incipient faults as early as possible under closed-loop control configurations. Firstly, a dynamic model of the suspension system for a three-car CRH2 multiple unit is set up, under which the external disturbances (i.e., track irregularities) and the actuator faults are further modeled. Then, an improved total measurable fault information residual (ToMFIR) based fault detection and estimation method is proposed, in which the restriction on fault type in the original method is removed. Finally, the obtained theoretical results are applied to a CRH2 HRVS simulation system. Results show that the proposed method can detect and estimate several frequent incipient faults effectively, outperforming the conventional observer-based or output residual-based fault detection and estimation methods.

Fault detection in multi-sensor networks based on multivariate time-series models and orthogonal transformations

We introduce the usage of multivariate orthogonal space transformations and vectorized time-series models in combination with data-driven system identification models to achieve an enhanced performance of residual-based fault detection in condition monitoring systems equipped with multi-sensor networks. Neither time-consuming annotated samples nor fault patterns/models need to be available, as our approach is solely based on on-line recorded data streams. The system identification step acts as a fusion operation by searching for relations and dependencies between sensor channels measuring the state of system variables. We therefore apply three different vectorized time-series variants: (i) non-linear finite impulse response models (NFIR) relying only on the lagged input variables, (ii) non-linear output error models (NOE), also including the lags of the own predictions and (iii) non-linear Box–Jenkins models (NBJ) which include the lags of the predictions errors as well. The use of multivariate orthogonal space transformations allows to produce more compact and accurate models due to an integrated dimensionality (noise) reduction step. Fault detection is conducted based on finding anomalies (untypical occurrences) in the temporal residual signal in incremental manner. Our experimental results achieved on four real-world condition monitoring scenarios employing multi-sensor network systems demonstrate that the Receiver Operating Characteristic (ROC) curves are improved over those ones achieved with native static models (w/o lags, w/o transformations) by about 20–30%.

Residual-based fault detection using soft computing techniques for condition monitoring at rolling mills

We propose a residual-based approach for fault detection at rolling mills based on data-driven soft computing techniques. It transforms the original measurement signals into a model space by identifying the multi-dimensional relationships contained in the system. Residuals, calculated as deviations from the identified relations and normalized with the model uncertainties, are analyzed on-line with incremental/decremental statistical techniques. The identification of the models and the fault detection concept are conducted solely based on the on-line recorded data streams. Thus, neither annotated samples nor fault patterns/models, which are often very time-intensive and costly to obtain, need to be available a priori. As model architectures, we used pure linear models, a new genetic variant of Box–Cox models (termed as Genetic Box–Cox) reflecting weak non-linearities and Takagi–Sugeno fuzzy models being able to express more complex non-linearities, which are trained with sparse learning techniques. This choice gives us a clue about the degree of non-linearity contained in the system. Our approach is compared with several state-of-the-art approaches including a PCA-based approach, a univariate time-series analysis, a one-class SVM (fault-free) pattern recognizer in the signal space and a combined approach based on time-series model parameter changes.

Data-Driven Residual-Based Fault Detection for Condition Monitoring in Rolling Mills

We propose a residual-based approach for fault detection in rolling mills which is based on data-driven soft computing techniques. The basic idea is to transform original measurement signals into a feature space by (i) identifying multi-dimensional relationships in the system, (ii) representing the nominal fault-free case, and (iii) analyzing residuals with incremental/decremental statistical techniques. Model identification and fault detection are conducted in a completely unsupervised manner, that is, solely based on the data streams recorded online. Thus, neither annotated samples nor fault patterns/models, which are often very time-intensive and costly to obtain, must be available a priori. We use purely linear models, a new genetic variant of Box-Cox models (termed Genetic Box-Cox) that consider weak non-linearities, and Takagi-Sugeno fuzzy models, which are able to express more complex non-linearities, trained with an extended version of SparseFIS. Using three typical scenarios from rolling mill production, we compare our method to state-of-the-art approaches that are based on principal components analysis and multi scale principal components analysis. The results show that our method outperforms these state-of-the-art approaches.

A novel model-based fault detection method for temperature sensor using fractal correlation dimension

The direct residual-based fault detection method compares the difference between measured and estimated data of a process variable. Its correct fault detection rate is low due to the noise in measured signals. A novel method using fractal correlation dimension (FCD) is developed, in which FCD deviation is adopted instead of direct residual. The method is validated by detecting fixed and drifting bias faults generated in supply air temperature sensor of air handling unit (AHU) system. The setting of three main parameters including embedding dimension, time delay parameter and length scale, is investigated to find out the influence on calculating FCD values. The results show that it is more efficient to detect relatively small bias fault under noise conditions, although it needs a period of time to collect measured data. As a promising and practical tool, a hybrid fault detection technique combining FCD with direct residual should be conducted in further investigation to identify the generated fault under inevitable noise conditions.

Fault detection of nonlinear dynamic systems with emperical input/output models

Predictive maintenance has become a familiar concept in industrial fault detection regime. The ability to detect early warning signals in systems in the form of small changes in dynamic behavior is essential to anticipate failures. This implies that the fault detection method must be well tuned to a particular process. In general, accurate system models are an essential part in residual based fault detection. However, in complex nonlinear systems, the development of accurate models can be very difficult, thus usually other approaches are often selected. As an alternative to the nonlinear analytical models, neural networks have shown significant potential in accurately representing nonlinear systems. This paper utilizes a non-typical model structure which includes internal states, so that the identification method is similar to a nonlinear extension to (linear) subspace identification. This model is then used with a moving horizon observer to generate residuals. The experimental section shows the observer implemented on a thermo fluid system, with several introduced faults.