Analysis of the reliability of a starter-generator using a dynamic Bayesian network

Lee, Dooyoul; Choi, Dongsu

doi:10.1016/j.ress.2019.106628

Cited by 29 publications

(10 citation statements)

References 10 publications

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“…There are many classical methods for parameter identification, such as the iterative regularization method [28], the Tikhonov regularization method [29], and Bayesian method [30]. When the problem of parameter identification has the characteristics of large number parameters to be identified, model complex and time-dependent data, dynamic Bayesian networks (DBNs) are an excellent choice [31]. Therefore, a DBN is adopted in this paper for parameter identification.…”

Section: Dynamic Bayesian Networkmentioning

confidence: 99%

Identification of Multiple Mechanical Properties of Laminates from a Single Compressive Test

et al. 2022

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In-plane elastic and interlaminar properties of composite laminates are commonly obtained through separate experiments. In this paper, a simultaneous identification method for both properties using a single experiment is proposed. The mechanical properties of laminates were treated as uncertainties and Bayesian inference was employed with measured strain-load curves in compression tests of laminates with embedded delamination. The strain–load curves were separated into two stages: the pre-delamination stage and the post-delamination stage. Sensitivity analysis was carried out to determine the critical properties at different stages, in order to alleviate the ill-posed problem in inference. Results showed that the in-plane Young’s modulus and shear modulus in elastic properties are dominant in the pre-delamination stage, and the interlaminar strength and type I fracture toughness in interlaminar properties are dominant in the post-delamination stage. Five times of property identification were carried out; the maximum coefficient of variation of identified properties was less than 1.11%, and the maximum error between the mean values of the identified properties and the ones from standard experiments was less than 5.44%. The proposed method can reduce time and cost in obtaining multiple mechanical properties of laminates.

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Section: Dynamic Bayesian Networkmentioning

confidence: 99%

Identification of Multiple Mechanical Properties of Laminates from a Single Compressive Test

et al. 2022

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“…The modern aircraft demand more and more electric energy [19,20]. Currently, there is a tendency to use DC generators as starter-generators.…”

Section: Importance Of the Starter-generatormentioning

confidence: 99%

Reliability Assessment of Aircraft Commutators

Wawrzyński

Zieja

Michalski³

2021

Energies

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The article describes the method of predicting the reliability and durability of an aircraft commutator, which is a primary source of electric energy in helicopters. Tests were conducted for 10 starter-generators. From this research it follows that the technical condition of brushes and bearings has a significant impact on the reliability of starter-generators. The reliability of starter-generators was determined based on the method consisting of two stages that was adopted: the first stage involved determining the density function of changes in diagnostic parameter depending on the operating time, but the second stage included the assessment of the reliability of bearings of the starter-generator taking into account the real flight profile. The first stage of the adopted method consisted of defining the dynamic model of changing the length of the starter-generator’s brush, which became the probabilistic model. Subsequently, based on differential equations, Fokker–Planck partial differential equation was derived, which describes the process of increasing the brush wear in a probabilistic way. This method enables the prediction of the residual durability of the helicopter’s starter-generator due to the change in a diagnostic parameter which is the wear of brushes during starter-generator operation. The second stage of this method allows determining the durability of starter-generator’s bearings building upon the average helicopter’s flight profile. Owing to the difficulty in measuring the wear of bearings, the relation between the durability of bearings and the temperature of surroundings can be applied by replacing the flight altitude with temperature. The reliability of the helicopter’s starter-generator was determined based on the serial-type reliability structure.

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“…For production environments, this concept is defined as the component that causes damage to engineering equipment, manufactured products or plant infrastructure, affecting operation, production and performance as well as the company's brand and reputation [21]. Planning of failure analysis within the context of risk and reliability is a strategy that contributes to minimizing the total cost, increasing the number of production, and producing higher quality products [28]. In addition, failure analysis is very effective in determining the price policies of companies [6].…”

Section: Introductionmentioning

confidence: 99%

A holistic FMEA approach by fuzzy-based Bayesian network and best–worst method

Yücesan

Gül

Çelik

2021

Complex Intell. Syst.

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Failure mode and effect analysis (FMEA) is a risk analysis tool widely used in the manufacturing industry. However, traditional FMEA has limitations such as the inability to deal with uncertain failure data including subjective evaluations of experts, the absence of weight values of risk parameters, and not considering the conditionality between failure events. In this paper, we propose a holistic FMEA to overcome these limitations. The proposed approach uses the fuzzy best–worst (FBWM) method in weighting three risk parameters of FMEA, which are severity (S), occurrence (O), and detection (D), and to find the preference values of the failure modes according to parameters S and D. On the other side, it uses the fuzzy Bayesian network (FBN) to determine occurrence probabilities of the failure modes. Experts use a procedure using linguistic variables whose corresponding values are expressed in trapezoidal fuzzy numbers, and determine the preference values of the failure modes according to parameter O in the constructed BN. Thus, the FBN including expert judgments and fuzzy set theory addresses uncertainty in failure data and includes a robust probabilistic risk analysis logic to capture the dependence between failure events. As a demonstration of the approach, a case study was conducted in an industrial kitchen equipment manufacturing facility. The results of the approach have also been compared with existed methods demonstrating its robustness.

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Analysis of the reliability of a starter-generator using a dynamic Bayesian network

Cited by 29 publications

References 10 publications

Identification of Multiple Mechanical Properties of Laminates from a Single Compressive Test

Identification of Multiple Mechanical Properties of Laminates from a Single Compressive Test

Reliability Assessment of Aircraft Commutators

A holistic FMEA approach by fuzzy-based Bayesian network and best–worst method

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