Multiple Sensor Fault Detection Research Articles

A hybrid multiple sensor fault detection, diagnosis and reconstruction algorithm for chiller plants

In a chiller plant, primary or critical sensors are used to control the system operation while secondary sensors are installed to monitor the performance/health of individual equipment. Current sensor fault detection and diagnosis (SFDD) approaches are not applicable to secondary sensors which usually are not involved in the system control. Consequently, a hybrid multiple sensor fault detection, diagnosis and reconstruction (HMSFDDR) algorithm for chiller plants was developed. Machine learning and pattern recognition were used to predict the primary sensor faults through the comparison of the weekly performance curves. With the primary sensor signals reconstructed, the secondary sensor faults were estimated based on mass and energy balance. By applying the algorithm with various logged plant data and comparison with site checking results, a maximum of 75% effectiveness could be achieved. The merits of the present approach were further justified through off-site sensor testing which reinforced the usefulness of proposed HMSFDDR algorithm.

Aircraft robust data-driven multiple sensor fault diagnosis based on optimality criteria

A general robust data-driven scheme for the Fault Detection, Isolation and Estimation of multiple sensor faults is proposed and validated using multi-flight data records. Robustness to modelling uncertainty and noise is achieved through an optimized data-driven design of the three blocks that constitute the scheme. First, a robust Fault Detection (FD) filter given by the linear combination of previously identified Analytical Redundancy Relationships (AARs) is derived as the solution of a multi-objective optimization where the minimum fault sensitivity is maximized while the standard deviation (STD) of the filtered error, in nominal condition, is minimized. Then, a Fault Pre-Isolation (FPI) block is introduced to select a restricted number of sensors containing with high likelihood the subset of the faulty sensors. In this phase, robustness is achieved through the data-driven design of a redundant number of Multiple-ARRs and a voting logic. Finally, the robust Fault Isolation (FI) is achieved relying on the design of a large collection of additional AARs whose fault signatures are specifically designed to optimize, at the same time, noise immunity while maximizing the decoupling of the (pre-isolated) fault directions. A procedure based on fault amplitude reconstruction is proposed to isolate the set of faulty sensors sequentially. The proposed scheme has been applied to the design of a multiple Fault Diagnosis scheme for a set of 8 sensors of a semi-autonomous aircraft basing on multi-flight data. Validation results are compared with state-of-the-art multiple Fault Diagnosis schemes.

Higher Order Sliding Mode Observer-Based Sensor Fault Detection in DC Microgrid’s Buck Converter

Fault detection in a Direct Current (DC) microgrid with multiple interconnections of distributed generation units (DGUs) is an interesting topic of research. The occurrence of any sensor fault in the DC microgrid should be detected immediately by the fault detection network to achieve an overall stable performance of the system. This work focuses on sensor fault diagnosis of voltage and current sensors in interconnected DGUs of the microgrid. Two separate higher order sliding mode observers (HOSM) based on model dynamics are designed to estimate the voltage and current and generate the residuals for detecting the faulty sensors in DGUs. Multiplicative single and multiple sensor faults are considered in voltage and current sensors. By appropriate selection of threshold, single and multiple sensor fault detection strategies are formulated. A hierarchical controller is designed to ensure equal sharing of current among the DGUs of the DC microgrid and stabilize the system. Simulations are performed to validate the proposed approach for various configurations of the DC microgrid under various load and off noise conditions.

Sensor Fault Reconstruction based on Adaptive Sliding Mode Observer for Forklift Fault-Tolerant Control System

For the problem of multiple sensor fault detection and reconstruction in the forklift fault-tolerant control system, a sliding mode observer (SMO) with adaptive regulation law is proposed. Based on the three-degree-of-freedom (3-DOF) model of forklift, a linear state equation with output disturbance is designed as its equivalent sensor fault model. The sensor fault is converted into an actuator fault by defining an auxiliary state variable as an output signal filter. Then the SMO-based method of sensor fault detection and reconstruction is given. Without knowing the upper bound of an unknown fault, an adaptive sliding mode observer (ASMO) can also be effective through the adaptive algorithm. Finally, experimental results further verify the effectiveness of the method, and provide a foundation for forklift fault-tolerant control.

Nonparametric Method for Aircraft Sensor Fault Real-Time Detection and Localization

This paper is a part of a series of articles on unified nonparametric methods in dynamic systems theory. Here the authors propose a new method for dynamic system sensor fault real-time detection and localization based only on some past measurements of monitored system input-output signals. The described nonparametric method needs no any priori information on system or sensor model parameters and does not require functional redundancy, identification, prediction, training or statistical calculations. An example of nonparametric detection and localization of aircraft multiple sensor faults is presented.

Multiple-sensor fault detection and isolation using video processing in production lines

ABSTRACT Production-line sensors are essential for synchronisation and execution of the correct workflow in a manufacturing process. Usually, sensor faults lead to serious delays, or even the complete termination of the manufacturing process, to carry out maintenance. Thus, sensor faults compromise the reliability of the manufacturing system and disturb the production schedule. This study proposes a multiple-sensor fault detection and isolation scheme for manufacturing series production lines. The proposed scheme adopts a global-redundancy method, using a digital camera and an ad-hoc video processing algorithm to detect and isolate faulty sensors. The main objective of this research is to preserve continuity of the production workflow and solve the problem of production delays and interruptions. Moreover, the scheme provides the possibility of online and post-process system maintenance. Further, the collected information on sensor false alarm rates can be used for a reliability analysis of the production line. The proposed scheme was tested using a laboratory production line model. The results show that the proposed scheme achieves the established objectives and improves the reliability of the manufacturing process.

Distributed Clustering-based Sensor Fault Diagnosis for HVAC Systems * *This work has been partially supported by the research project “Stability and Control of Power Networks with Energy Storage (STABLE-NET),” funded by the RCUK Energy Programme (contract no: EP/L014343/1) and by the Italian MIUR Project SEAL -Smart&safe Energy-aware Assisted Living (SCN-00398)

Abstract The paper presents a distributed Sensor Fault Diagnosis architecture for Industrial Wireless Sensor Networks monitoring HVAC systems, by exploiting a recently proposed distributed clustering method. The approach allows the detection and isolation of multiple sensor faults and considers the possible presence of modeling uncertainties and disturbances. Detectability and isolability conditions are provided. Simulation results show the effectiveness of the proposed method for an HVAC system.

Quadrotor Sensor Fault Diagnosis with Experimental Results

This paper presents a fault detection, isolation, and estimation scheme for sensor bias faults in accelerometer and gyroscope measurements of quadrotor unmanned air vehicles (UAVs). Based on sliding-mode observer techniques, a robust estimation of the quadrotor roll and pitch angles is obtained by using only accelerometer measurements. Then, a diagnostic scheme is developed for detecting, isolating, and estimating sensor bias faults in the gyroscope and accelerometer measurements. Structured residuals are generated, allowing the detection and isolation of multiple simultaneous sensor faults under consideration. After the faults are detected and isolated, two nonlinear estimators are employed to provide an estimate of the unknown fault magnitude. The stability and estimation performance properties of the nonlinear estimators are established. The sensor fault diagnosis algorithm is implemented and evaluated through experimental results using a real-time indoor quadrotor test environment.

Decentralized Isolation of Multiple Sensor Faults in Large-Scale Interconnected Nonlinear Systems

This paper presents the design and analysis of a methodology for detecting and isolating multiple sensor faults in large-scale interconnected nonlinear systems. The backbone of the proposed decentralized methodology is the design of a local sensor fault diagnosis agent dedicated to each interconnected subsystem, without the need to communicate with neighboring agents. Each local sensor fault diagnosis agent is responsible for detecting and isolating multiple faults in the local set of sensors. The local sensor fault diagnosis agent consists of a bank of modules that monitor smaller groups of sensors in the corresponding local sensor set. The detection of faults in each of the sensor groups is conducted using robust analytical redundancy relations, formulated by structured residuals and adaptive thresholds. The multiple sensor fault isolation in each local sensor fault diagnosis agent is realized by aggregating the decisions of the modules and applying a diagnostic reasoning-based decision logic. The performance of the proposed diagnostic scheme is analyzed with respect to sensor fault detectability and multiple sensor fault isolability. A simulation example of two interconnected robot manipulators is used to illustrate the application of the multiple sensor fault detection and isolation methodology.

Multi-sensor Distributed Fault Detection Method Based on Subjective Bayesian Reasoning

Each sensor detects information presents problems under uncertainty in computerized numerical control(CNC)machining center fault prediction. Aiming at this problem, multi-sensor distributed fault detection method based on uncertainty reasoning is proposed. The algorithm by using subjective Bayesian reasoning, acquire the local detection device of decision rules, and select the local decision rules suitable to the fusion center, finally a global decision is produced. The complex vertical machining center as an example, the distributed multiple sensor fault detection platform is built. Fault sample is acquired using multi-sensor sample on different machine running state and running environment. Experiments show that in the fault diagnosis system contains a lot of information uncertainty, distributed detection fusion algorithm based on subjective Bayesian inference has the advantages of high recognition rate of fault information, diagnosis speed. Diagnosis error rate of multi-sensor distributed detection fusion algorithm is significantly lower than that of single sensor and the serial structure.

Adaptive approximation for multiple sensor fault detection and isolation of nonlinear uncertain systems.

This paper presents an adaptive approximation-based design methodology and analytical results for distributed detection and isolation of multiple sensor faults in a class of nonlinear uncertain systems. During the initial stage of the nonlinear system operation, adaptive approximation is used for online learning of the modeling uncertainty. Then, local sensor fault detection and isolation (SFDI) modules are designed using a dedicated nonlinear observer scheme. The multiple sensor fault isolation process is enhanced by deriving a combinatorial decision logic that integrates information from local SFDI modules. The performance of the proposed diagnostic scheme is analyzed in terms of conditions for ensuring fault detectability and isolability. A simulation example of a single-link robotic arm is used to illustrate the application of the adaptive approximation-based SFDI methodology and its effectiveness in detecting and isolating multiple sensor faults.

Multiple sensor fault diagnosis by evolving data-driven approach

Sensors are indispensable components of modern plants and processes and their reliability is vital to ensure reliable and safe operation of complex systems. In this paper, the problem of design and development of a data-driven Multiple Sensor Fault Detection and Isolation (MSFDI) algorithm for nonlinear processes is investigated. The proposed scheme is based on an evolving multi-Takagi Sugeno framework in which each sensor output is estimated using a model derived from the available input/output measurement data. Our proposed MSFDI algorithm is applied to Continuous-Flow Stirred-Tank Reactor (CFSTR). Simulation results demonstrate and validate the performance capabilities of our proposed MSFDI algorithm.

Detection, isolation and identification of multiple actuator and sensor faults in nonlinear dynamic systems: Application to a waste water treatment process

The goal in many fault detection and isolation schemes is to increase the isolation and identification speed. This paper, presents a new approach of a nonlinear model based adaptive observer method, for detection, isolation and identification of actuator and sensor faults. Firstly, we will design a new method for the actuator fault problem where, after the fault detection and before the fault isolation, we will try to estimate the output of the instrument. The method is based on the formation of nonlinear observer banks where each bank isolates each actuator fault. Secondly, for the sensor problem we will reformulate the system by introducing a new state variable, so that an augmented system can be constructed to treat sensor faults as actuator faults. A method based on the design of an adaptive observers’ bank will be used for the fault treatment. These approaches use the system model and the outputs of the adaptive observers to generate residues. Residuals are defined in such way to isolate the faulty instrument after detecting the fault occurrence. The advantages of these methods are that we can treat not only single actuator and sensor faults but also multiple faults, more over the isolation time has been decreased. In this study, we consider that only abrupt faults in the system can occur. The validity of the methods will be tested firstly in simulation by using a nonlinear model of waste water treatment process with and without measurement noise and secondly with the same nonlinear model but by using this time real data.

Multiple sensor fault detection and isolation of an air quality monitoring network using RBF-NLPCA model

This paper presents a data-driven method based on non-linear principal component analysis to detect and isolate multiple sensor faults. The RBF-NLPCA model is obtained by combining a principal curve algorithm and two three-layer radial basis function (RBF) networks. The reconstruction approach for multiple sensors is proposed in the non-linear case and successfully applied for multiple sensor fault detection and isolation of an air quality monitoring network. The proposed approach reduces considerably the number of reconstruction combinations and allows to determine replacement values for the faulty sensors.

Multiple sensor fault detection and isolation of an air quality monitoring network using RBF-NLPCA model

This paper presents a data-driven method based on nonlinear principal component analysis to detect and isolate multiple sensor faults. The RBF-NLPCA model is obtained by combining a principal curve algorithm and two three layer radial basis function (RBF) networks. The reconstruction approach for multiple sensors is proposed in the non linear case and successfully applied for multiple sensor fault detection and isolation of an air quality monitoring network. The proposed approach reduces considerably the number of reconstruction combinations and allows to determine replacement values for the faulty sensors.

A hybrid intelligent system for fault detection and sensor fusion

In this paper, an efficient new hybrid approach for multiple sensor fusion and fault detection is proposed, addressing the problem with multiple faults, which is based on conventional fuzzy soft clustering and artificial immune systems. For this new approach, requires no prior knowledge or information about the sensors, or the system behavior, and no learning processes are required. The proposed hybrid approach consists of two main phases. In the first phase a single fuser for the input sensor signals is generated using the fuzzy clustering c-means algorithm. The fused output is based on the cluster centers that contain the maximum number of the input elements. In the second phase a fault detector was generated base on the artificial immune system AIS.