Fault detection and isolation of smart actuators using bond graphs and external models

Bouamama, Belkacem Ould; Medjaher, Kamal; Bayart, Mireille; Samantaray, Arun Kumar; Conrard, Blaise

doi:10.1016/j.conengprac.2004.03.003

Cited by 80 publications

(35 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A minimum, a multiplicative, an additive, and a mixed approach were developed and specifically applied to monitor the smart actuator studied in [101]. Also Ould Bouamama et al [103] dealt with fault detection and isolation (FDI) of smart actuators. They combined bond graphs and external models to assess different faults.…”

Section: Smart Diagnosismentioning

confidence: 99%

RETRACTED: Review on Valve Stiction. Part I: From Modeling to Smart Diagnosis

Capaci

Scali

2015

Processes

View full text Add to dashboard Cite

Abstract:Valve stiction is indicated as one of the main problems affecting control loop performance and then product quality. Therefore, it is important to detect this phenomenon as early as possible, distinguish it from other causes, and suggest the correct action to the operator in order to fix it. It is also very desirable to give an estimate of stiction amount, in order to be able to follow its evolution in time to allow the scheduling of valve maintenance or different operations, if necessary. This paper, in two parts, is a review of the state of the art about the phenomenon of stiction from its basic characterization to smart diagnosis, including modeling, detection techniques, quantification, compensation and a description of commercial software packages. In particular, Part I of the study analyzes the most significant works appearing in the recent literature, pointing out analogies and differences among various techniques, showing more appealing features and possible points of weakness. The review also includes an illustration of the main features of performance monitoring systems proposed by major software houses. Finally, the paper gives indications on future research trends and potential advantages for loop diagnosis when additional measurements are available, as in newly designed plants with valve positioners and smart instrumentation. In Part II, performance of some well-established methods for stiction quantification are compared by applications to different industrial datasets. OPEN ACCESSProcesses 2015, 3 423

show abstract

Section: Smart Diagnosismentioning

confidence: 99%

RETRACTED: Review on Valve Stiction. Part I: From Modeling to Smart Diagnosis

Capaci

Scali

2015

Processes

View full text Add to dashboard Cite

show abstract

“…The condition of the pneumatic and hydraulic cylinders (Wang et al, 2004), and digitally controlled valves (Karpenko et al, 2003) were the main focus of the studies. Some of the other considered faults were leakage of the seals (Nakutis & Kaškonas, 2005Yang, 2006;Sepasi & Sassani, 2010), friction increase (Wang et al, 2004;Nogami et al, 1995) and other malfunctions (Bouamama et al, 2005). The monitored signals can be divided into two groups according to their frequencies.…”

Section: Introductionmentioning

confidence: 99%

“…Acoustic emission is an excellent example of high frequency monitoring signal (Yang, 2006;Chena et al, 2007). The frequency of the pressure, flow, and timing signals are low (Sepasi & Sassani, 2010;Nogami et al, 1995;Bouamama et al, 2005;Nakutis & Kaškonas, 2005Wang et al, 2004;Karpenko et al, 2003;Li & Kao, 2005;McGhee et al, 1997). The gathered signals are encoded to obtain their most descriptive features.…”

Section: Introductionmentioning

confidence: 99%

“…The gathered signals are encoded to obtain their most descriptive features. The encoded signals were classified by using various classification techniques such as ANNs (Karpenko et al, 2003;Nakutis & Kaškonas, 2003;Sepasi & Sassani, 2010;Nogami et al, 1995;McGhee et al, 1997), fuzzy method (Mendonca et al, 2009;, neuro fuzzy method (Shi & Sepehri, 2004;, statistical technique (Song et al, 2003), bond graphs (Bouamama et al, 2005), genetic programming (Wang et al, 2004;Yang, 2006), and expert/intelligent systems (Chen & Mo, 2004). It is not difficult to develop programs for classification of the sensory signals of pneumatic systems.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Conditioning Monitoring and Fault Diagnosis for a Servo-Pneumatic System with Artificial Neural Network Algorithms

Demetgül¹,

Taşkın²,

Tansel³

2011

Artificial Neural Networks - Industrial and Control Engineering Applications

View full text Add to dashboard Cite

“…For technical or financial reasons, this solution is reserved for high-tech industries or those with high technological risks. Moreover, this hardware redundancy does not make it possible to protect against a failure of certain common elements of the measurement chain [7][8][9][10][11][12]. Moreover, analytical redundancy has the advantage of not increasing the cost of the installation and freeing itself from the hardware constraints.…”

Section: Introductionmentioning

confidence: 99%

Robust Diagnosis of Vehicular Active Suspension System Using Bond Graph Approach

Sellami¹,

Mzoughi²,

Mami³

2018

IJET

View full text Add to dashboard Cite

Diagnostic systems play a major role in the safety of industrial systems and the availability of its equipment. Rapid detecti on (as soon as possible) to the operator of the detected deviations (defects) in relation to the expected nominal behavior is fundamental for the implementation of preventive and corrective actions on industrial systems. These industrial systems are governed by several physical phenomena and various technological components, which is why the Bond Graph tool, based on an energy and multi-physical analysis, is well suited. In this article, we will discuss the problem of diagnosing an active suspension system of a vehicle by presenting diagnostic methods. Then, we introduce the bond graph tool for the robust diagnosis of the system. Finally, the extension of the deterministic models presented to models integrating uncertain element s (Fractional linear transformations LFT) and the generation of robust analytical redundancy relationships are also detailed.

show abstract

Fault detection and isolation of smart actuators using bond graphs and external models

Cited by 80 publications

References 13 publications

RETRACTED: Review on Valve Stiction. Part I: From Modeling to Smart Diagnosis

RETRACTED: Review on Valve Stiction. Part I: From Modeling to Smart Diagnosis

Conditioning Monitoring and Fault Diagnosis for a Servo-Pneumatic System with Artificial Neural Network Algorithms

Robust Diagnosis of Vehicular Active Suspension System Using Bond Graph Approach

Contact Info

Product

Resources

About