Data fusion of acceleration and velocity features (dFAVF) approach for fault diagnosis in rotating machines

Luwei, Kenisuomo; Sinha, Jyoti; Yunusa‐Kaltungo, Akilu; Elbhbah, Keri

doi:10.1051/matecconf/201821121005

Cited by 7 publications

(8 citation statements)

References 7 publications

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“…Therefore, the model is not tested blindly. This model was then further modified by Luwei, Sinha et al [26]. The features are slightly modified based on rotor dynamics.…”

Section: Approach 1: Time Domain Featuresmentioning

confidence: 99%

“…The recent research studies by Espinoza Sepulveda and Sinha [25] and Luwei, Sinha et al [26] are used as the basis for further development. The ANN approach is used as the AI-based ML tool for this study.…”

Section: Introductionmentioning

confidence: 99%

“…Espinoza Sepulveda and Sinha [25] have used only time domain parameters in the VML model, which work well for the same machine conditions. Similarly, the time and frequency domain parameters suggested by Luwei, Sinha et al [26] are also used to develop the AI-based VML model. Once again, this model performed accurately when tested at the same machine conditions that are used during training.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Parameter Optimisation in the Vibration-Based Machine Learning Model for Accurate and Reliable Faults Diagnosis in Rotating Machines

Sepulveda¹,

Sinha²

2020

Machines

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Artificial intelligence (AI)-based machine learning (ML) models seem to be the future for most of the applications. Recent research effort has also been made on the application of these AI and ML methods in the vibration-based faults diagnosis (VFD) in rotating machines. Several research studies have been published over the last decade on this topic. However, most of the studies are data driven, and the vibration-based ML (VML) model is generally developed on a typical machine. The developed VML model may not predict faults accurately if applied on other identical machines or a machine with different operation conditions or both. Therefore, the current research is on the development of a VML model by optimising the vibration parameters based on the dynamics of the machine. The developed model is then blindly tested at different machine operation conditions to show the robustness and reliability of the proposed VML model.

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“…Therefore, the model is not tested blindly. This model was then further modified by Luwei, Sinha et al [26]. The features are slightly modified based on rotor dynamics.…”

Section: Approach 1: Time Domain Featuresmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Parameter Optimisation in the Vibration-Based Machine Learning Model for Accurate and Reliable Faults Diagnosis in Rotating Machines

Sepulveda¹,

Sinha²

2020

Machines

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show abstract

“…However, the proposed compound methods are composed of diverse algorithms, which may result in the complexity of computational system for fault detection. For the purpose of detecting consistently and integrally, there should be some concise algorithms in data preprocessing, or one algorithm to realize the monitoring and detection of the whole system [15,16,17,18]. For example, Yunusa-Kaltungo et al [19] proposed an improved composite spectrum data fusion technique to retain amplitude and phase information by applying cross power spectrum density to fault diagnosis in rotating machines.…”

Section: Introductionmentioning

confidence: 99%

Sensors Information Fusion System with Fault Detection Based on Multi-Manifold Regularization Neighborhood Preserving Embedding

Jiang

Chen

et al. 2019

Sensors

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Electrical drive systems play an increasingly important role in high-speed trains. The whole system is equipped with sensors that support complicated information fusion, which means the performance around this system ought to be monitored especially during incipient changes. In such situation, it is crucial to distinguish faulty state from observed normal state because of the dire consequences closed-loop faults might bring. In this research, an optimal neighborhood preserving embedding (NPE) method called multi-manifold regularization NPE (MMRNPE) is proposed to detect various faults in an electrical drive sensor information fusion system. By taking locality preserving embedding into account, the proposed methodology extends the united application of Euclidean distance of both designated points and paired points, which guarantees the access to both local and global sensor information. Meanwhile, this structure fuses several manifolds to extract their own features. In addition, parameters are allocated in diverse manifolds to seek an optimal combination of manifolds while entropy of information with parameters is also selected to avoid the overweight of single manifold. Moreover, an experimental test based on the platform was built to validate the MMRNPE approach and demonstrate the effectiveness of the fault detection. Results and observations show that the proposed MMRNPE offers a better fault detection representation in comparison with NPE.

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“…Once the CCS harmonic amplitudes and their corresponding SEs of interest (depending on the fault types considered, e.g., low frequency for rotor-related and higher frequency for gear faults) have been obtained, Stage 2 of fusion involves their standardisation, dimensionality reduction, and harmonisation based on PCA [49][50][51] and ANN [52][53][54]. The computational steps required for Stage 2 are described by Equations ( 7)- (11).…”

mentioning

confidence: 99%

An Automated Data Fusion-Based Gear Faults Classification Framework in Rotating Machines

Cao

Yunusa‐Kaltungo

2021

Sensors

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The feasibility and usefulness of frequency domain fusion of data from multiple vibration sensors installed on typical industrial rotating machines, based on coherent composite spectrum (CCS) as well as poly-coherent composite spectrum (pCCS) techniques, have been well-iterated by earlier studies. However, all previous endeavours have been limited to rotor faults, thereby raising questions about the proficiency of the approach for classifying faults related to other critical rotating machine components such as gearboxes. Besides the restriction in scope of the founding CCS and pCCS studies on rotor-related faults, their diagnosis approach was manually implemented, which could be unrealistic when faced with routine condition monitoring of multi-component industrial rotating machines, which often entails high-frequency sampling at multiple locations. In order to alleviate these challenges, this paper introduced an automated framework that encompassed feature generation through CCS, data dimensionality reduction through principal component analysis (PCA), and faults classification using artificial neural network (ANN). The outcomes of the automated approach are a set of visualised decision maps representing individually simulated scenarios, which simplifies and illustrates the decision rules of the faults characterisation framework. Additionally, the proposed approach minimises diagnosis-related downtime by allowing asset operators to easily identify anomalies at their incipient stages without necessarily possessing vibration monitoring expertise. Building upon the encouraging results obtained from the preceding part of this approach that was limited to well-known rotor-related faults, the proposed framework was significantly extended to include experimental and open-source gear fault data. The results show that in addition to early established rotor-related faults classification, the approach described here can also effectively and automatically classify gearbox faults, thereby improving the robustness.

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Data fusion of acceleration and velocity features (dFAVF) approach for fault diagnosis in rotating machines

Cited by 7 publications

References 7 publications

Parameter Optimisation in the Vibration-Based Machine Learning Model for Accurate and Reliable Faults Diagnosis in Rotating Machines

Parameter Optimisation in the Vibration-Based Machine Learning Model for Accurate and Reliable Faults Diagnosis in Rotating Machines

Sensors Information Fusion System with Fault Detection Based on Multi-Manifold Regularization Neighborhood Preserving Embedding

An Automated Data Fusion-Based Gear Faults Classification Framework in Rotating Machines

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