Sensor Fault Diagnosis in Wind Turbines

This paper covers model-based fault detection and isolation for linear and nonlinear distributed parameter systems (DPS). The first part mainly deals with actuator, sensor and state fault detection and isolation for a class of DPS represented by a set of coupled linear partial differential equations (PDE). A filter based observer is designed based on the linear PDE representation using which a detection residual is generated. A fault is detected when the magnitude of the detection residual exceeds a detection threshold. Upon detection, several isolation estimators are designed using filters whose output residuals are compared with predefined isolation thresholds. A fault on a linear DPS is declared to be of certain type if the corresponding isolation estimator output residual is below its isolation threshold while the other fault isolation estimator output residual is above its threshold. Next, the fault location is determined when a state fault is identified. The second part of this paper focuses on fault detection and isolation of nonlinear DPS by using a Luenberger type observer. Here fault isolation framework is introduced to isolate actuator, sensor and state faults with isolability condition by using additional boundary measurements and without filters. Finally, the effectiveness of the proposed fault detection and isolation schemes for both linear and nonlinear DPS are demonstrated through simulation.

In this paper, a fault detection and isolation (FDI) methodology based on an immune system (IS) inspired mechanism known as the dendritic cell algorithm (DCA) is developed and implemented. Our proposed DCA-based FDI methodology is then applied to a well-known wind turbine test model. The proposed DCA-based scheme performs both detection as well as isolation of sensor faults given dual sensor redundancy, unlike other works in the literature that only address the fault detection problem and rely on analytical redundancy approach for accomplishing the fault isolation task. A nonparametric statistical comparison test is also performed to compare the performance of the DCA-based FDI scheme with another IS-based scheme known as the negative selection algorithm. Through extensive simulation case study scenarios the capabilities and performance of our proposed methodologies have been fully demonstrated and justified.

ABSTRACTQuick and timely fault detection is of great importance in control systems reliability. Undetected faulty sensors could result in irreparable damages. Although fault detection and isolation (FDI) methods in control systems have received much attention in the last decade, these techniques have not been applied for some classes of nonlinear systems yet. This paper deals with the issues of sensor fault detection and isolation for a class of Lipschitz uncertain nonlinear system. By introducing a coordinate transformation matrix for states and output, the original system is first divided into two subsystems. The first subsystem is affected by uncertainty and disturbance. The second subsystem just has sensor faults. The nonlinear term is separated to linear and pure nonlinear parts. For fault detection, two sliding mode observers (SMO) are designed for the two subsystems. The stability condition is obtained based on the Lyapunov approach. The necessary matrices and parameters are obtained by solving the linear matrix inequality (LMI) problem. Furthermore, two sliding mode observers are designed for fault isolation. Finally, the effectiveness of the proposed approach is illustrated by simulation examples.

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

Sensor fault detection and isolation via high-gain observers: Application to a double-pipe heat exchanger

To improve the reliability of wind turbines, various condition monitoring systems (CMSs) have been developed and most of them transmit data using wired communication channels. Recently, wireless sensor networks (WSNs) have been used to transmit data in wind turbine CMS due to the low cost and easy deployment feature of WSNs. However, since wind turbines are installed in harsh environments, the sensors and sensor nodes used in the WSN-based wind turbine CMSs are easily subject to faults, leading to corruption of the signals used for condition monitoring, which decreases the reliability of the CMS. This paper proposes a three-stage method for the detection and isolation of three most common sensor faults, i.e., SHORT fault, CONSTANT fault, and NOISE fault, in WSN-based wind turbine CMS. The proposed sensor fault detection and isolation greatly increases the accuracy and reliability of wind turbine CMSs. Data collected from wind turbines in the field are used to validate the effectiveness of the proposed method.

This paper addresses the Fault Detection and Isolation (FDI) of the problem of multiple non-concurrent faults on actuator and sensor of quadrotors, which is described by a nonlinear model. To the best of authors' knowledge, this paper provides, for the first time, a solution to the FDI problem for quadrotors affected by both actuator and sensor faults. The literature proposes FDI schemes dedicated only to actuators or only to sensors, indeed. The Non Linear Geometric Approach is based on the mathematical model of the system and allows to determine detection filters sensitive to a selected set of fault and structurally decoupled from the other faults (out of the selected set) thus providing the base for the fault isolation. Several simulation trials have been performed and the analysis of the results proves that the developed diagnostic system represents an effective solution to the problem of multiple actuator and sensor fault detection and isolation for quadrotors.

This paper considers model-based fault detection of large-scale (possibly rank-deficient) dynamic systems. Assuming only global (and not local) observability over a sensor network, we introduce a single time-scale networked estimator/observer. Sensors take local outputs/measurements of system states with partial observability and share their information (including estimation and/or output) over a communication network, and gain distributed observability. We define the conditions on the network structure ensuring distributed observability and stabilising the error dynamics. However, system outputs are prone to faults and uncertainties, which affect the state estimation of all sensors as a consequence of communicating (possibly) faulty data. From the cyber-physical-systems (CPS) perspective, such faults add bias to the data transferred from the physical layer (dynamic system) to the cyber layer (sensor network). In this work, we propose a localised fault detection and isolation (FDI) mechanism at sensors to secure distributed estimation. This protocol enables every sensor to locally identify the possible fault at the sensor measurement, and, via local detection and isolation, to prevent the spread of biased/faulty information over the network. This distributed isolation and localisation of fault follows from our partial observability assumption instead of full observability at every sensor. Then, other sensors can estimate/track the system by using observationally-equivalent output information to recover for possible loss of observability. In particular, we study rank-deficient systems as they are known to demand more information-sharing, and thus, are more vulnerable to the spread of possible faults over the network. One challenge is the detection of faults in the presence of system/output noise without making (simplifying and unrealistic) upper-bound assumptions on the noise support. We resolve this by adopting probabilistic threshold designs on the residuals. Further, we show that additive faults at rank-deficiency-related outputs affect the residuals at all sensors, a consequence that mandates more constraints on the (distributed) FDI strategy. We address this problem by constrained LMI design of the feedback gain matrix. Finally, we design q-redundant distributed estimators, resilient to isolation/removal of up to q number of faulty sensors, and further, we consider thresholding residual history over a sliding time-window, known as the stateful FDI.

An approach for model-based fault detection and isolation (FDI) of sensor and process faults for nonlinear processes is presented. The process is decomposed into several sub-processes and for each process a nonlinear model is identified. This model bank consisting of fuzzy models (Takagi-Sugeno type) is used to generate several different estimates for process outputs and states. Comparing these estimates with the actual measured ones leads to residuals which indicate the state of the system and provide information about the source of possible faults. The two ways to implement a model, as a parallel or as a series-parallel model lead to different FDI results. Hence, this different sensitivity is also investigated in this contribution. The practical applicability is illustrated on an industrial scale thermal plant. Here, seven different process faults and eight different sensor faults can be detected.

In this paper, an online sensor fault detection and isolation (FDI) scheme is proposed based on an emerging Artificial Immune System (AIS) algorithm, namely Dendritic Cell Algorithm (DCA). Our proposed methodology is utilized in a distributed manner in order to perform sensor FDI in complex systems. The proposed methodology is then applied to a wind turbine benchmark model in order to demonstrate its capabilities. 2016 IEEE.

In this paper the problem of fault diagnosis in an aircraft jet engine is investigated by using an intelligent-based methodology. The proposed fault detection and isolation (FDI) scheme is based on the multiple model approach and utilizes autoassociative neural networks (AANNs). This methodology consists of a bank of AANNs and provides a novel integrated solution to the problem of both sensor and component fault detection and isolation even though possibly both engine and sensor faults may occur concurrently. Moreover, the proposed algorithm can be used for sensor data validation and correction as the first step for health monitoring of jet engines. We have also presented a comparison between our proposed approach and another commonly used neural network scheme known as dynamic neural networks to demonstrate the advantages and capabilities of our approach. Various simulations are carried out to demonstrate the performance capabilities of our proposed fault detection and isolation scheme.

This paper presents a model based fault detection and isolation (FDI) of internal sensors for a powered wheelchair. Three fault modes of five internal sensors (two wheel-encoders, two steering-potentiometers and one yaw-rate gyro) are handled: hard fault, noise fault and scale fault modes. On the hard fault the sensor output is stuck at a constant value. The noise fault causes the sensor output with large noise. The scale fault changes the sensor parameter from the fault-free. The hard and noise faults are diagnosed based on their mode probability estimated with the variable structure interacting multi-model (VSIMM) estimator. The scale fault is diagnosed based on the wheelchair velocity estimated with a laser scan matching. For fault-tolerant operation of the wheelchair even in the presence of sensor faults, output estimates of faulty sensors are exploited to control the wheelchair. Experimental results validate the effectiveness of our FDI method.

Sensor fault detection and isolation filter for polytopic LPV systems: A winding machine application

<p>In the recent years, wireless sensor network technology (WSN) has been widely adopted in precision agriculture for determining the needs of the soil in term of water by monitoring some environmental parameters. To do this, WSN is constructed using several sensor nodes; these small sensing devices are prone to failure and may produce erroneous measurements. To ensure good management of freshwater, the network service quality is necessary. To avoid the degradation of service, the detection of the faulty sensor in WSN is required. In this paper, a fault detection and isolation (FDI) algorithm derived from a parity space approach and based on direct redundancy is proposed toward detecting and isolating sensor fault in WSN. In laboratory experiments, the proposed FDI algorithm proved its effectiveness.</p>

This paper presents a Fault Detection and Isolation (FDI) method for Air Data Sensors (ADS) of aircraft. In the most general case, fault detection of these sensors on modern aircraft is performed by a logic that selects one of, or combines three redundant measurements. Such a method is compliant with current airworthiness regulations. However, in the framework of the global aircraft optimization for future and upcoming aircraft, it could be required, e.g. to extend the availability of sensor measurements. So, an improvement of the state of practice could be useful. Introducing a form of analytical redundancy of these measurements can increase the fault detection performance and result in a weight saving of the aircraft because there is no necessity anymore to increase the number of sensors. Furthermore, the analytical redundancy can contribute to the structural design optimization. The analytical redundancy in this method is introduced using an adaptive form of the Extended Kalman Filter (EKF). This EKF uses the kinematic relations of the aircraft and makes a state reconstruction from the available measurements possible. From this estimated state, an estimated output is calculated and compared to the measurements. Through observing a metric derived from the innovation of the Extended Kalman Filter (EKF), the performance of each of the redundant sensors is monitored. This metric is then used to automatically isolate the failing sensors.