Image-Based Versus Signal-Based Sensors for Machine Fault Detection and Isolation

2014 ◽  
Author(s):  
Heshan Fernando ◽  
Vedang Chauhan ◽  
Brian Surgenor
Keyword(s):  
Fault Detection ◽  
High Speed ◽  
Fault Isolation ◽  
Video Data ◽  
Fault Detection And Isolation ◽  
Single Camera ◽  
Multiple Faults ◽  
Multiple Sensors ◽  
Machine Fault ◽  
And Performance

This paper presents the results of a comparative study that investigated the use of image-based and signal-based sensors for fault detection and fault isolation of visually-cued faults on an automated assembly machine. The machine assembles 8 mm circular parts, from a bulk-supply, onto continuously moving carriers at a rate of over 100 assemblies per minute. Common faults on the machine include part jams and ejected parts that occur at different locations on the machine. Two sensor systems are installed on the machine for detecting and isolating these faults: an image-based system consisting of a single camera and a signal-based sensor system consisting of multiple greyscale sensors and limit switches. The requirements and performance of both systems are compared for detecting six faults on the assembly machine. It is found that both methods are able to effectively detect the faults but they differ greatly in terms of cost, ease of implementation, detection time and fault isolation capability. The conventional signal-based sensors are low in cost, simple to implement and require little computing power, but the installation is intrusive to the machine and readings from multiple sensors are required for faster fault detection and isolation. The more sophisticated image-based system requires an expensive, high-resolution, high-speed camera and significantly more processing power to detect the same faults; however, the system is not intrusive to the machine, fault isolation becomes a simpler problem with video data, and the single camera is able to detect multiple faults in its field of view.

Multiple faults detection and isolation approach applied to a kraft recovery boiler

TAPPI Journal ◽  
2014 ◽  
Vol 13 (1) ◽  
pp. 33-41
Author(s):  
YVON THARRAULT ◽  
MOULOUD AMAZOUZ
Keyword(s):  
Fault Detection ◽  
Early Detection ◽  
False Alarm ◽  
False Alarm Rate ◽  
Principal Component ◽  
Fault Isolation ◽  
Fault Detection And Isolation ◽  
Detection Scheme ◽  
Multiple Faults ◽  
Recovery Boiler

Recovery boilers play a key role in chemical pulp mills. Early detection of defects, such as water leaks, in a recovery boiler is critical to the prevention of explosions, which can occur when water reaches the molten smelt bed of the boiler. Early detection is difficult to achieve because of the complexity and the multitude of recovery boiler operating parameters. Multiple faults can occur in multiple components of the boiler simultaneously, and an efficient and robust fault isolation method is needed. In this paper, we present a new fault detection and isolation scheme for multiple faults. The proposed approach is based on principal component analysis (PCA), a popular fault detection technique. For fault detection, the Mahalanobis distance with an exponentially weighted moving average filter to reduce the false alarm rate is used. This filter is used to adapt the sensitivity of the fault detection scheme versus false alarm rate. For fault isolation, the reconstruction-based contribution is used. To avoid a combinatorial excess of faulty scenarios related to multiple faults, an iterative approach is used. This new method was validated using real data from a pulp and paper mill in Canada. The results demonstrate that the proposed method can effectively detect sensor faults and water leakage.

scholarly journals Application of a Fault Detection and Isolation System on a Rotary Machine

2013 ◽  
Vol 2013 ◽  
pp. 1-11 ◽  
Author(s):  
Silvia M. Zanoli ◽  
Giacomo Astolfi
Keyword(s):  
Fault Detection ◽  
Centrifugal Compressor ◽  
Principal Component ◽  
Fault Isolation ◽  
Fault Detection And Isolation ◽  
Isolation System ◽  
Multiple Faults ◽  
Rotary Machine ◽  
Model Free ◽  
Refinery Plant

The paper illustrates the design and the implementation of a Fault Detection and Isolation (FDI) system to a rotary machine like a multishaft centrifugal compressor. A model-free approach, that is, the Principal Component Analysis (PCA), has been employed to solve the fault detection issue. For the fault isolation purpose structured residuals have been adopted while an adaptive threshold has been designed in order to detect and to isolate the faults. To prove the goodness of the proposed FDI system, historical data of a nitrogen centrifugal compressor employed in a refinery plant are considered. Tests results show that detection and isolation of single as well as multiple faults are successfully achieved.

scholarly journals Fault Identification and Isolation in Dynamic Systems Using Multiple Models

2019 ◽  
Vol 11 (1) ◽  
Author(s):  
Olivia Maria Alves Coelho ◽  
Wlamir O. L. Vianna ◽  
Takashi Yoneyama
Keyword(s):  
Dynamic Systems ◽  
Computational Models ◽  
Failure Modes ◽  
Fault Isolation ◽  
Fault Detection And Isolation ◽  
Multiple Models ◽  
Multiple Faults ◽  
Industrial Systems ◽  
Isolation Techniques ◽  
And Performance

The demand for more reliability, safety and performance in industrial systems is rapidly increasing every day. The early detection of faults can avoid catastrophic events and the identification of the fault nature and severity can lead to the most appropriated and efficient maintenance task. Thus, an enhanced system diagnosis feature has the potential to increase safety and reduce the operational costs. In this context, fault detection and isolation techniques are used as the basis for building powerful decision making tools. This work's objective is to identify and isolate multiple faults in dynamic systems through signal processing. An approach based on a multiple-models architecture is considered whereas the plant output signals is compared with simulation data from a set of models representing the failure modes being analysed. The Autonomous Multiple Models (AMM) technique is chosen for further residue estimation and fault isolation. A case study using computational models representing an electro-mechanical system is carried out in order to validate the proposed method and evaluate its performance and limitations such as failure modes not mapped through the models and its capability to handle concurrent faults.

scholarly journals Estimation and Fault Diagnosis of Lithium-Ion Batteries: A Fractional-Order System Approach

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Shulan Kong ◽  
Mehrdad Saif ◽  
Guozeng Cui
Keyword(s):  
Fault Diagnosis ◽  
Fault Detection ◽  
Fractional Order ◽  
Sliding Mode ◽  
Estimation Error ◽  
Lithium Ion ◽  
Fault Isolation ◽  
Sliding Mode Observer ◽  
Fault Detection And Isolation ◽  
Order System

This study investigates estimation and fault diagnosis of fractional-order Lithium-ion battery system. Two simple and common types of observers are designed to address the design of fault diagnosis and estimation for the fractional-order systems. Fractional-order Luenberger observers are employed to generate residuals which are then used to investigate the feasibility of model based fault detection and isolation. Once a fault is detected and isolated, a fractional-order sliding mode observer is constructed to provide an estimate of the isolated fault. The paper presents some theoretical results for designing stable observers and fault estimators. In particular, the notion of stability in the sense of Mittag-Leffler is first introduced to discuss the state estimation error dynamics. Overall, the design of the Luenberger observer as well as the sliding mode observer can accomplish fault detection, fault isolation, and estimation. The effectiveness of the proposed strategy on a three-cell battery string system is demonstrated.

A PCA Based Fault Detection and Isolation Method for Train Locating System

2010 ◽  
Vol 20-23 ◽  
pp. 688-693
Author(s):  
Jiang Liu ◽  
Bai Gen Cai ◽  
Tao Tang ◽  
Jian Wang
Keyword(s):  
Fault Detection ◽  
Fault Tolerant ◽  
Fault Detection And Isolation ◽  
Practical Application ◽  
Isolation Method ◽  
Efficiency Factors ◽  
Simulation Results ◽  
And Performance ◽  
Locating System ◽  
Fault Detectors

Fault tolerance is crucial to the operating safety and performance of train locating system. Based on the requirements of reliability and safety for train locating, the fault characteristics of location measuring sensors are analyzed. Based on the structure of the train locating system, the fault-tolerant design of the system is given with the location filtering module for case, in which six fault detectors are employed to determine the configuration of the module. Then a PCA based fault detection and isolation method is proposed with Hawkins T2 statistics and the corresponding control limit. By dynamically adjusting the efficiency factors, fault could be detected and isolated as prior defined isolating strategies, and then the fault tolerant performance will be guaranteed. Simulation results demonstrate the high fault tolerant ability of the proposed approach and certain practical application value.

Fault Detection and Isolation for Lithium-Ion Battery System Using Structural Analysis and Sequential Residual Generation

2014 ◽  
Author(s):  
Zhentong Liu ◽  
Qadeer Ahmed ◽  
Giorgio Rizzoni ◽  
Hongwen He
Keyword(s):  
Structural Analysis ◽  
Fault Detection ◽  
Cooling System ◽  
Hazard Analysis ◽  
Lithium Ion ◽  
Test Construction ◽  
Fault Isolation ◽  
Fault Detection And Isolation ◽  
Battery Cell ◽  
Battery Systems

This paper presents a systematic methodology based on structural analysis and sequential residual generators to design a Fault Detection and Isolation (FDI) scheme for nonlinear battery systems. The faults to be diagnosed are highlighted using a detailed hazard analysis conducted for battery systems. The developed methodology includes four steps: candidate residual generators generation, residual generators selection, diagnostic test construction and fault isolation. State transformation is employed to make the residuals realizable. The simulation results show that the proposed FDI scheme successfully detects and isolates the faults injected in the battery cell with cooling system at different times. In addition, there are no false or missed detections of the faults.

Robust Model-Based Fault Detection and Isolation of a Six Degree of Freedom Helicopter

2020 ◽  
Author(s):  
Lázaro F. Sansón ◽  
Victor A. de Campos ◽  
Alain S. Potts
Keyword(s):  
Fault Detection ◽  
Fault Isolation ◽  
Degree Of Freedom ◽  
Fault Detection And Isolation ◽  
Decoupling Method ◽  
Strongly Nonlinear ◽  
Robust Model ◽  
Six Degree Of Freedom ◽  
Safety Systems ◽  
Strong Control

Helicopters are high cost and safety systems with a strong control system designed to maintain the helicopter performance, stability, and flight qualities. However, there exist faults that negatively aect the helicopter desirable behaviour; therefore, fault detection and isolation must be done to early detect, isolate and eliminate these faults. Because of helicopters are strongly nonlinear systems, and are aected by uncertainties and by external disturbances aswind bursts, robust residuals generation is required to correctly detect and isolate faults in the helicopter actuators and sensors. This paper leads with the robust fault detection and isolation of a six-degree of freedom helicopter benchmark using the disturbance decoupling method and the unknown input observer robust residuals generator. A generalized observer scheme is employed for fault isolation purposes.

scholarly journals A Hamiltonian Approach to Fault Isolation in a Planar Vertical Take–Off and Landing Aircraft Model

2015 ◽  
Vol 25 (1) ◽  
pp. 65-76 ◽  
Author(s):  
Luis H. Rodriguez-Alfaro ◽  
Efrain Alcorta-Garcia ◽  
David Lara ◽  
Gerardo Romero
Keyword(s):  
Hamiltonian System ◽  
Nonlinear Systems ◽  
Fault Detection ◽  
Fault Isolation ◽  
Fault Detection And Isolation ◽  
System Model ◽  
Actuator Faults ◽  
Hamiltonian Approach ◽  
Aircraft Model ◽  
Fault Sensitivity

Abstract The problem of fault detection and isolation in a class of nonlinear systems having a Hamiltonian representation is considered. In particular, a model of a planar vertical take-off and landing aircraft with sensor and actuator faults is studied. A Hamiltonian representation is derived from an Euler-Lagrange representation of the system model considered. In this form, nonlinear decoupling is applied in order to obtain subsystems with (as much as possible) specific fault sensitivity properties. The resulting decoupled subsystem is represented as a Hamiltonian system and observer-based residual generators are designed. The results are presented through simulations to show the effectiveness of the proposed approach.

Performance Analysis of Fault Detection and Identification for Multiple Faults in GNSS and GNSS/INS Integration

2015 ◽  
Vol 9 (1) ◽  
Author(s):  
Muwaffaq Alqurashi ◽  
Jinling Wang
Keyword(s):  
Performance Analysis ◽  
Fault Detection ◽  
Real Time ◽  
Correlation Coefficients ◽  
Geometric Constraints ◽  
Fault Detection And Isolation ◽  
Multiple Fault ◽  
Multiple Faults ◽  
Detection And Identification ◽  
Fault Detection And Identification

AbstractFor positioning, navigation and timing (PNT) purposes, GNSS or GNSS/INS integration is utilised to provide real-time solutions. However, any potential sensor failures or faulty measurements due to malfunctions of sensor components or harsh operating environments may cause unsatisfactory estimation for PNT parameters. The inability for immediate detecting faulty measurements or sensor component failures will reduce the overall performance of the system. So, real time detection and identification of faulty measurements is required to make the system more accurate and reliable for different applications that need real time solutions such as real time mapping for safety or emergency purposes. Consequently, it is necessary to implement an online fault detection and isolation (FDI) algorithm which is a statistic-based approach to detect and identify multiple faults.However, further investigations on the performance of the FDI for multiple fault scenarios is still required. In this paper, the performance of the FDI method under multiple fault scenarios is evaluated, e.g., for two, three and four faults in the GNSS and GNSS/INS measurements under different conditions of visible satellites and satellites geometry. Besides, the reliability (e.g., MDB) and separability (correlation coefficients between faults detection statistics) measures are also investigated to measure the capability of the FDI method. A performance analysis of the FDI method is conducted under the geometric constraints, to show the importance of the FDI method in terms of fault detectability and separability for robust positioning and navigation for real time applications.

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