Vibration Analysis in Turbomachines Using Machine Learning Techniques

Pinheiro, Allan Alves; Brandao, Iago Modesto; Costa, Cesar da

doi:10.24018/ejeng.2019.4.2.1128

Cited by 4 publications

(3 citation statements)

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“…In [93][94][95][96][97][98][99], vibration analysis is a widely used method of PdM. The analytical method uses sensors to measure the vibrations of machinery and identify possible problem areas, such as bearing failure or misalignment on various machines, including motors, pumps, and gearboxes.…”

Section: State-of-the-art Techniques For Predictive Maintenancementioning

confidence: 99%

Artificial Intelligence for Predictive Maintenance Applications: Key Components, Trustworthiness, and Future Trends

Ucar,

Karakose,

Kırımça

2024

Applied Sciences

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Predictive maintenance (PdM) is a policy applying data and analytics to predict when one of the components in a real system has been destroyed, and some anomalies appear so that maintenance can be performed before a breakdown takes place. Using cutting-edge technologies like data analytics and artificial intelligence (AI) enhances the performance and accuracy of predictive maintenance systems and increases their autonomy and adaptability in complex and dynamic working environments. This paper reviews the recent developments in AI-based PdM, focusing on key components, trustworthiness, and future trends. The state-of-the-art (SOTA) techniques, challenges, and opportunities associated with AI-based PdM are first analyzed. The integration of AI technologies into PdM in real-world applications, the human–robot interaction, the ethical issues emerging from using AI, and the testing and validation abilities of the developed policies are later discussed. This study exhibits the potential working areas for future research, such as digital twin, metaverse, generative AI, collaborative robots (cobots), blockchain technology, trustworthy AI, and Industrial Internet of Things (IIoT), utilizing a comprehensive survey of the current SOTA techniques, opportunities, and challenges allied with AI-based PdM.

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Section: State-of-the-art Techniques For Predictive Maintenancementioning

confidence: 99%

Artificial Intelligence for Predictive Maintenance Applications: Key Components, Trustworthiness, and Future Trends

Ucar,

Karakose,

Kırımça

2024

Applied Sciences

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“…Possible features include time-domain features [2,3], but usually frequency domain features are used. Approaches typically use a Fourier transform for feature extraction such as in [4,5], but all these assume a known frequency fingerprint to be extracted. In order to specifically construct a feature-space that best suits the application some works define their own feature extraction process [3,6].…”

Section: Introductionmentioning

confidence: 99%

Model-Free Data Mining of Families of Rotating Machinery

Hofer

Mohrenschildt

2022

Applied Sciences

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Machines designed to perform the same tasks using different technologies can be organized into families based on their similarities or differences. We are interested in identifying common properties and differences of such machines from raw sensor data for analysis and fault diagnostics. The usual first step is a feature extraction process that requires an understanding of the machine’s harmonics, bearing frequencies, etc. In this paper, we present a model-free path from the raw sensor data to statistically meaningful feature vectors. This is accomplished by defining a transform independent of the operating frequency and performing statistical reductions to identify the components with the largest variances, resulting in a low dimensional statistically meaningful feature space. To obtain an insight into the family relationships we perform a clustering. As the data set has some labeled characteristics we define an entropy-based measure to evaluate a clustering using the a priori-known labels, resulting in a symmetric measurement uniquely defining the clustering goal. Applying this hierarchically we obtain the family tree. The methods are presented can be applied in general situations. As a case study we apply them to a real data set of vibrating screens.

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“…Many studies have proposed a similar method where the equivalent load is combined with modal expansion and an optimization algorithm such as least squares for identifying the unbalance parameters [14][15][16][17]. Other studies have utilized residual mapping techniques [18,19], M-estimator [20], extended [1] and augmented [21] Kalman filters, and machine learning techniques [22][23][24] for estimating unbalance. Some researchers such as Sinha et al [25,26], Edwards et al [27], and Lees et al [28,29] also proposed methods that can identify the unbalance parameters with a single measure- S. Bastakoti, T. Choudhury, R. Viitala, E. Kurvinen, and J. Sopanen ment, which makes them suitable for identifying the initial unbalance at commissioning stages as well.…”

Section: Introductionmentioning

confidence: 99%

Bulletin of the Polish Academy of Sciences: Technical Sciences

Bastakoti

Choudhury

Viitala

et al. 2021

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To achieve acceptable dynamical behavior for large rotating machines operating at subcritical speeds, the balancing quality check at the planned service speed in the installation location is often demanded for machines such as turbo-generators or high-speed machines. While most studies investigate the balancing quality at critical speeds, only a few studies have investigated this aspect using numerical methods at operational speed. This study proposes a novel, model-based method for inversely estimating initial residual unbalance in one and two planes after initial grade balancing for large flexible rotors operating at the service speeds. The method utilizes vibration measurements from two planes in any single direction, combined with a finite element model of the rotor to inversely determine the residual unbalance in one and two planes. This method can be practically used to determine the initial and residual unbalance after the balancing process, and further it can be used for condition-based monitoring of the unbalance state of the rotor.

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Vibration Analysis in Turbomachines Using Machine Learning Techniques

Cited by 4 publications

References 0 publications

Artificial Intelligence for Predictive Maintenance Applications: Key Components, Trustworthiness, and Future Trends

Artificial Intelligence for Predictive Maintenance Applications: Key Components, Trustworthiness, and Future Trends

Model-Free Data Mining of Families of Rotating Machinery

Bulletin of the Polish Academy of Sciences: Technical Sciences

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