Sinusoidal synthesis based adaptive tracking for rotating machinery fault detection

Li, Gang; McDonald, Geoff L.; Zhao, Qing

doi:10.1016/j.ymssp.2016.06.019

Cited by 12 publications

(7 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The faults such as rotor unbalance and rotor misalignment diagnosed by this method are consistent with the actual fault types. Li et al [61] of the University of Alberta in Canada came up with an adaptive tracking technique based on sinusoidal synthesis, which uses the sinusoidal characteristics of vibration signals to transform a nonlinear problem into a time-domain linear adaptive problem based on state space. Lu et al [62] put forward a genetic algorithm based on dynamic search strategy, which showed good feature extraction and selection ability in two kinds of fault experiments of rotor unbalanced vibration and bearing damage.…”

Section: Feature Extraction Based On Intelligent Algorithmmentioning

confidence: 99%

A Review on Self-Recovery Regulation (SR) Technique for Unbalance Vibration of High-End Equipment

Pan

Huo

et al. 2020

Chin. J. Mech. Eng.

View full text Add to dashboard Cite

The high-end equipment represented by high-end machine tools and aero-engines is the core component of the national intelligent manufacturing plan, and the mass unbalance is the main reason for its excessive vibration, that seriously impacts the operation efficiency and running life of the equipment. In order to change the traditional way that the fault of equipment can only be repaired by human, the self-recovery mechanism of human and animal are given to the equipment in this paper, which forms the self-recovery regulation (SR) system for unbalance vibration of high-end equipment. The system can online generate the self-recovery force to restrain the unbalance vibration of the equipment in operation, which is an important direction for the development of the equipment to the advanced intelligent stage. Based on the basic principles of SR technique, the typical engineering application cases of this technique in the field of aeroengine and high-end machine tools are introduced, and four related studies promoting the development of this technique are summarized and analyzed in turn. It includes feature extraction, imbalance location, regulation method and balancing actuator. Self-recovery Regulation (SR) Technique is an important way to realize intelligent manufacturing and intelligent maintenance. Relevant research can lay a technical foundation for the development of high-end equipment with self-health function.

show abstract

Section: Feature Extraction Based On Intelligent Algorithmmentioning

confidence: 99%

A Review on Self-Recovery Regulation (SR) Technique for Unbalance Vibration of High-End Equipment

Pan

Huo

et al. 2020

Chin. J. Mech. Eng.

View full text Add to dashboard Cite

show abstract

“…The fundamental premise of VM is to adequately understand, track, and determine the trend of these characteristics for individual critical assets, so as to determine deviations at incipient stages before the occurrence of catastrophic failures. Despite the huge successes recorded with well-established VM techniques in time [ 16 , 17 ], frequency [ 18 ], and time–frequency [ 19 ] domains, the rigour often associated with individualised synthesis of large volumes of data acquired from each measurement location on a typical rotating machine can prolong decision-making, which may lead to fatal consequences when dealing with critical safety systems. To further compound this problem, most modern-day industrial rotating machines are multi-component (e.g., gears, bearings, drive belts, rotors, electric motors, couplings, etc.)…”

Section: Introductionmentioning

confidence: 99%

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

Cao

Yunusa‐Kaltungo

2021

Sensors

View full text Add to dashboard Cite

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.

show abstract

“…Bearings are the most critical components and widely used in rotating machinery, whose health conditions, for example, the fault degree in different places under different motor speeds and loads, may have a huge effect on the performance, reliability, and residual life of the equipment [1] or even can lead to heavy casualties [2][3][4]. Hence, it is important to diagnose bearings under different working conditions.…”

Section: Introductionmentioning

confidence: 99%

“…In order to obtain effective and robust transferable feature representation and improve the quality of fault diagnosis, our work aims to reduce the impact of discrepancies from both the marginal and conditional distributions between training and test domains by resorting the pseudo labels of test data [26] on diagnosis, and these pseudo labels can be obtained from a base classifier (NN classifier) built on the labeled training data to predict the fully unlabeled test data. Thus, the final optimization problem (6) in this paper comprised (2) and (4).…”

mentioning

confidence: 99%

See 1 more Smart Citation

Bearing Fault Diagnosis Based on Domain Adaptation Using Transferable Features under Different Working Conditions

Tong

Zhang

et al. 2018

Shock and Vibration

View full text Add to dashboard Cite

Bearing failure is the most common failure mode in rotating machinery and can result in large financial losses or even casualties. However, complex structures around bearing and actual variable working conditions can lead to large distribution difference of vibration signal between a training set and a test set, which causes the accuracy-dropping problem of fault diagnosis. Thus, how to improve efficiently the performance of bearing fault diagnosis under different working conditions is always a primary challenge. In this paper, a novel bearing fault diagnosis under different working conditions method is proposed based on domain adaptation using transferable features(DATF). The datasets of normal bearing and faulty bearings are obtained through the fast Fourier transformation (FFT) of raw vibration signals under different motor speeds and load conditions. Then we reduce marginal and conditional distributions simultaneously across domains based on maximum mean discrepancy (MMD) in feature space by refining pseudo test labels, which can be obtained by the nearest-neighbor (NN) classifier built on training data, and then a robust transferable feature representation for training and test domains is achieved after several iterations. With the help of the NN classifier trained on transferable features, bearing fault categories are identified accurately in final. Extensive experiment results show that the proposed method under different working conditions can identify the bearing faults accurately and outperforms obviously competitive approaches.

show abstract

Sinusoidal synthesis based adaptive tracking for rotating machinery fault detection

Cited by 12 publications

References 32 publications

A Review on Self-Recovery Regulation (SR) Technique for Unbalance Vibration of High-End Equipment

A Review on Self-Recovery Regulation (SR) Technique for Unbalance Vibration of High-End Equipment

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

Bearing Fault Diagnosis Based on Domain Adaptation Using Transferable Features under Different Working Conditions

Contact Info

Product

Resources

About