Sensor Location Optimization for Fault Diagnosis in Multi-Fixture Assembly Systems

1998 ◽  
Vol 120 (4) ◽  
pp. 781-792 ◽  
Author(s):  
A. Khan ◽  
D. Ceglarek ◽  
J. Ni
Keyword(s):  
Fault Diagnosis ◽  
Fault Isolation ◽  
Hierarchical Optimization ◽  
Sensor Location ◽  
Sheet Metal Parts ◽  
Automotive Body ◽  
Assembly Sequences ◽  
Sensor Configuration ◽  
Effectiveness Index ◽  
Isolation Performance

The effectiveness of fault diagnosis in assembly is contingent on the effectiveness of the sensor measurement of assembled parts. Using a diagnosability enhancement methodology for a single fixture, a means to achieve an optimal sensor configuration for a multi-fixture assembly of sheet metal parts is proposed. A Hierarchical Group description of the assembly is used to build a State-Transition representation which, with fixture CAD information, is used in multi-level hierarchical optimization to arrive at the optima. A defined Coverage Effectiveness Index quantifies fault isolation performance. The index also serves to evaluate the performance effectiveness of the measurement station location and change in the sensor number. The approach has significant utility in automotive body assembly where system complexity makes the choice of sensor location vital to fault isolation performance. Examples using multi-fixture simulated and industrial automotive body assembly sequences are provided to illustrate the methodology.

Sensor Optimization for Fault Diagnosis in Multi-Fixture Assembly Systems With Distributed Sensing

1998 ◽  
Vol 122 (1) ◽  
pp. 215-226 ◽  
Author(s):  
A. Khan ◽  
D. Ceglarek
Keyword(s):  
Optimization Procedure ◽  
Hierarchical Optimization ◽  
Dual Purpose ◽  
Distributed Sensor ◽  
Sensor Optimization ◽  
Multi Level ◽  
Sensor Configuration ◽  
Limited Sensing ◽  
Sheet Metal Assembly ◽  
Efficient Distribution

Sensing for the system-wide diagnosis of dimensional faults in multi-fixture sheet metal assembly presents significant issues of complexity due to the number of levels of assembly and the number of possible faults at each level. The traditional allocation of sensing at a single measurement station is no longer sufficient to guarantee adequate fault diagnostic information for the increased parts and levels of a complex assembly system architecture. This creates a need for an efficient distribution of limited sensing resources to multiple measurement locations in assembly. The proposed methodology achieves adequate diagnostic performance by configuring sensing to provide an optimally distinctive signature for each fault in assembly. A multi-level, two-step, hierarchical optimization procedure using problem decomposition, based on assembly structure data derived directly from CAD files, is used to obtain such a novel, distributed sensor configuration. Diagnosability performance is quantified in the form of a defined index, which serves the dual purpose of guiding the optimization and establishing the diagnostic worth of any candidate sensor distribution. Examples, using a multi-fixture layout, are presented to illustrate the methodology. [S1087-1357(00)70801-X]

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.

scholarly journals Hierarchical Analysis of Thorax Models to Measure Tidal Volume

2018 ◽  
Vol 4 (1) ◽  
pp. 429-432
Author(s):  
Bernhard Laufer ◽  
Sabine Krueger-Ziolek ◽  
Knut Moeller ◽  
Paul David Docherty ◽  
Fabian Hoeflinger ◽  
...  
Keyword(s):  
Tidal Volume ◽  
Motion Tracking ◽  
Optimization Method ◽  
Optimal Combination ◽  
Hierarchical Optimization ◽  
Hierarchical Analysis ◽  
Hierarchical Method ◽  
Computational Costs ◽  
Sensor Configuration ◽  
Time Required

AbstractMotion tracking of thorax kinematics can be used to determine respiration. However, determining a minimal sensor configuration from 64 candidate sensor locations is associated with high computational costs. Hence, a hierarchical optimization method was proposed to determine the optimal combination of sensors. The hierarchical method was assessed by its ability to quickly determine the sensor combination that will yield optimal modelled tidal volume compared to body plethysmograph measurements. This method was able to find the optimal sensor combinations, in approximately 2% of the estimated time required by an exhaustive search.

Sensor Location for Effective Fault Diagnosis in Micro Chemical Processes

2009 ◽  
Vol 42 (11) ◽  
pp. 309-314
Author(s):  
Osamu Tonomura ◽  
Satoshi Nagahara ◽  
Jun-ichi Kano ◽  
Manabu Kano ◽  
Shinji Hasebe
Keyword(s):  
Fault Diagnosis ◽  
Chemical Processes ◽  
Sensor Location

Fault Diagnosis for a Three Mass Torsion Oscillator Using a Bank of Fault Models

2008 ◽  
Author(s):  
Joachim Baehr ◽  
Rolf Isermann
Keyword(s):  
Fault Diagnosis ◽  
Fault Model ◽  
Fault Isolation ◽  
Model Parameters ◽  
Equation Approach ◽  
Fault Models ◽  
Sensor Signal ◽  
Torsion Oscillator ◽  
Diagnosis Method

A fault diagnosis method for a three mass torsion oscillator is considered which is subject to different additive faults. By using a bank of fault models three faults of different type are detected, isolated and identified in size and time of occurrence. The bank of fault models is formed by a model of each considered fault. Comparison of simulated fault model outputs and measured signals leads to fault isolation. Fault size and time of occurrence are identified by a parity equation approach and used as fault model parameters. The method is capable to perform the tasks with use of one actuator and one sensor signal. It is shown that common approaches for fault isolation can not be used due to the small number of measured signals.

Quality-related fault diagnosis based on k-nearest neighbor rule for non-linear industrial processes

2021 ◽  
Vol 17 (11) ◽  
pp. 155014772110559
Author(s):  
Zelin Ren ◽  
Yongqiang Tang ◽  
Wensheng Zhang
Keyword(s):  
Fault Diagnosis ◽  
Fault Detection ◽  
Least Squares ◽  
Partial Least Squares ◽  
Nearest Neighbor ◽  
Fault Isolation ◽  
K Nearest Neighbor ◽  
Kernel Partial Least Squares ◽  
Non Linear ◽  
Nearest Neighbor Rule

The fault diagnosis approaches based on k-nearest neighbor rule have been widely researched for industrial processes and achieve excellent performance. However, for quality-related fault diagnosis, the approaches using k-nearest neighbor rule have been still not sufficiently studied. To tackle this problem, in this article, we propose a novel quality-related fault diagnosis framework, which is made up of two parts: fault detection and fault isolation. In the fault detection stage, we innovatively propose a novel non-linear quality-related fault detection method called kernel partial least squares- k-nearest neighbor rule, which organically incorporates k-nearest neighbor rule with kernel partial least squares. Specifically, we first employ kernel partial least squares to establish a non-linear regression model between quality variables and process variables. After that, the statistics and thresholds corresponding to process space and predicted quality space are appropriately designed by adopting k-nearest neighbor rule. In the fault isolation stage, in order to match our proposed non-linear quality-related fault detection method kernel partial least squares- k-nearest neighbor seamlessly, we propose a modified variable contributions by k-nearest neighbor (VCkNN) fault isolation method called modified variable contributions by k-nearest neighbor (MVCkNN), which elaborately introduces the idea of the accumulative relative contribution rate into VC k-nearest neighbor, such that the smearing effect caused by the normal distribution hypothesis of VC k-nearest neighbor can be mitigated effectively. Finally, a widely used numerical example and the Tennessee Eastman process are employed to verify the effectiveness of our proposed approach.

Characterization of Solenoid On-Off Valve Faults: A Faster Analytical Modeling Approach

2021 ◽  
Author(s):  
Hao Tian ◽  
Sichen Li ◽  
Jianbo Liu ◽  
Jiaoyi Hou ◽  
Yongjun Gong
Keyword(s):  
Fault Diagnosis ◽  
Power Systems ◽  
Experimental System ◽  
Solenoid Valve ◽  
Fault Isolation ◽  
Sensor Data ◽  
Analytical Models ◽  
Lumped Mass ◽  
Valve Body ◽  
Computationally Intensive

Abstract Solenoid valves are enabling components for flow and motion control in fluid power systems. It is important for reduction of a machinery’s “off” time if quick fault diagnosis of solenoid valves can be performed on site. Traditional fault diagnosis usually involves signal convolution, machine learning, or fuzzy logics, which can achieve over 90% accuracy but are all computationally intensive, and may be difficult to achieve rapid analysis on site with portable electronics. Among various kinds of faults of solenoid valves, the most common ones are the leakage caused by poor sealing between the poppet and the sleeve, stiction caused by foreign matters in the valve body, or solenoid failure. This paper proposed to simplify the fault diagnosis process by derivation of analytical models of fault characteristics of solenoid on-off valve based on electromagnetics and lumped mass fluid mechanics. Firstly, according to the structure of the solenoid valve, the electromagnetic model of the armature, solenoid, and the air gap is deduced. Secondly, five features from two sensor data are constructed. Fault isolation matrix, tested features are also defined. Thirdly, experimental system was setup to acquire the key threshold values for membership function definition. Finally, two validation tests were conducted and the initial results showed that the proposed method is capable of detecting solenoid valve stiction of various degrees.

Research of Pipeline Fault Diagnosis for Liquid Rocket Propulsion System

2012 ◽  
Vol 232 ◽  
pp. 305-309
Author(s):  
Yan Jun Li ◽  
Xiao Hui Peng ◽  
Yu Qiang Cheng ◽  
Jian Jun Wu
Keyword(s):  
Fault Diagnosis ◽  
False Alarm ◽  
Test Data ◽  
Simulation Analysis ◽  
Fault Simulation ◽  
Fault Isolation ◽  
Necessary Condition ◽  
Rocket Propulsion ◽  
Diagnosis Algorithm ◽  
Liquid Rocket

The applications of healthy monitoring and fault diagnosis's system can enhance the reliability and safety of the whole vehicle, which has significance to detect and isolate fault as early as possible and that tragedy can be avoided. In this paper, pipeline fault simulation analysis and fault diagnosis for LRE is studied as direct-inverse problem. Firstly, the failure model was constituted for pipeline to analysis the necessary condition for fault isolation. Then the conclusion that strategy of fault diagnosis was build by analysis the simulation result based on AMESim. Finally, the fault diagnosis algorithm was validated by test data. The results indicate that the fault diagnosis algorithm can detect fault exactly and effectively. There are no false alarm to normal test data and no false alarm to other components fault. Consequently the fault isolation result could be reached.

Application of ANN in Hydraulic Pressure Control Fault Diagnosis System

2013 ◽  
Vol 347-350 ◽  
pp. 864-868
Author(s):  
Xiao Yu Zhang ◽  
Li Li Ding
Keyword(s):  
Neural Network ◽  
Control System ◽  
Fault Diagnosis ◽  
Pressure Control ◽  
Fault Isolation ◽  
Hydraulic Pressure ◽  
Diagnosis System ◽  
Pressure Control System ◽  
Fault Diagnosis System ◽  
Artificial Neural Network Ann

The existing hydraulic pressure control fault diagnosis system is effective on fault detection, but the fault isolation capability is bad. In order to improve the capability of the fault isolation, the artificial neural network (ANN) is used in the fault diagnosis system. Aimed at the representative diagnosis of the hydraulic pressure control system, the three layers feedback network is adopted, the basic theory of conjugate gradient BP neural network is explained in detail, and the key techniques are introduced. Five types of typical faults of hydraulic pressure control system can be distinguished easily by it, the faults diagnosis efficiency is higher 30% than ever and the fault diagnosis capability is better 80% than before.

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