Classification of unbalance and misalignment in induction motors using orbital analysis and associative memories

Hernández, José Juan Carbajal; Sánchez-Fernández, Luis Pastor; Hernández-Bautista, Ignacio; Juárez, José de Jesús Medel; Sánchez-Pérez, Luis Alejandro

doi:10.1016/j.neucom.2015.06.094

Cited by 21 publications

(9 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the experiments the data were collected from an experimental test bench (Fig. 3), which represents in a reduced scale a real turbo machine, based on the Jeffcott model [16]. Its axis being represented by a rotor and a disk with several holes for simulation of unbalance.…”

Section: Methodsmentioning

confidence: 99%

Vibration Analysis in Turbomachines Using Machine Learning Techniques

Pinheiro¹,

Brandao²,

Costa³

2019

EJERS

View full text Add to dashboard Cite

This study proposes a method for diagnosing faults in turbomachines using machine learning techniques. In this study, a support vector machine-SVM algorithm is proposed for fault diagnosis of rotor rotation imbalance. Recently, support vector machines (SVMs) have become one of the most popular classification methods in vibration analysis technology. Axis unbalance defect is classified using support vector machines. The experimental data is derived from the turbomachine model of the rigid-shaft rotor and the flexible bearings, and the experimental setup for vibration analysis. Several situations of unbalance defects have been successfully detected.

show abstract

Section: Methodsmentioning

confidence: 99%

Vibration Analysis in Turbomachines Using Machine Learning Techniques

Pinheiro¹,

Brandao²,

Costa³

2019

EJERS

View full text Add to dashboard Cite

show abstract

“…To solve the problem of the data class imbalance, SMOTE [11] and other data generation algorithms can be used to generate a new data of a minority class to balance the class distribution. However, the use of these methods may change the original distribution of the data, thereby reducing the overall prediction accuracy.…”

Section: Introductionmentioning

confidence: 99%

Linkboost: A Link Prediction Algorithm to Solve the Problem of Network Vulnerability in Cases Involving Incomplete Information

Jia

Liu

et al. 2020

Complexity

View full text Add to dashboard Cite

e vulnerability of network information systems has attracted considerable research attention in various domains including financial networks, transportation networks, and infrastructure systems. To comprehensively investigate the network vulnerability, well-designed attack strategies are necessary. However, it is difficult to formulate a global attack strategy as the complete information of the network is usually unavailable. To overcome this limitation, this paper proposes a novel prediction algorithm named Linkboost, which, by predicting the hidden edges of the network, can complement the seemingly missing but potentially existing connections of the network with limited information. e key aspect of this algorithm is that it can deal with the imbalanced class distribution present in the network data. e proposed approach was tested on several types of networks, and the experimental results indicated that the proposed algorithm can successfully enhance the destruction rate of the network even with incomplete information. Furthermore, when the proportion of the missing information is relatively small, the proposed attack strategy relying on the high degree nodes performs even better than that with complete information. is finding suggests that the nodes important to the network structure and connectivity can be more easily identified by the links added by Linkboost. erefore, the use of Linkboost can provide useful insight into the operation guidance and design of a more effective attack strategy.

show abstract

“…In the past few years artificial intelligence methods have been exploited to utilize the shaft orbits for automatic fault identification and classification of turbomachinery (e.g., Carbajal-Herná ndez et al 2016, Jeong et al 2016, Wu et al 2018, Khodja et al 2019. Carbajal-Herná ndez et al (2016) developed the Lernmatrix associative memory approach associated with orbital pattern recognition to classify the imbalance and misalignment faults of induction motors. Jeong et al (2016) presented a convolutional neural network (CNN) based deep learning model to identify the fault of rotating machinery via orbit images.…”

Section: Introductionmentioning

confidence: 99%

Novel Orbit-based CNN Model for Automatic Fault Identification of Rotating Machines

Jiang

Wang

Zhao³

et al. 2020

PHM_CONF

View full text Add to dashboard Cite

Various faults in high-fidelity turbomachinery such as steam turbines and centrifugal compressors usually result in unplanned outage thus lowering the reliability and productivity while largely increasing the maintenance costs. Condition monitoring has been increasingly applied to provide early alerting on component faults by using the vibration signals. However, each type of fault in different types of rotating machines usually require an individual model to isolate the damage for accurate condition monitoring, which require costly computation efforts and resources due to the data uncertainties and modeling complexity. This paper presents a generalized deep learning methodology for accurately automatic diagnostics of various faults in general rotating machines by utilizing the shaft orbits generated from vibration signals, considering the high non-linearity and uncertainty of the sensed vibration signals. The sensor anomalies and environmental noise in the vibration signals are first addressed through waveform compensation and Bayesian wavelet noise reduction filtering. Shaft orbit images are generated from the cleansed vibration data collected from different turbomachinery with various fault modes. A multi-layer convolutional neural network model is then developed to classify and identify the shaft orbit images of each fault. Finally, the fault diagnosis of rotating machinery is realized through the automated identification process. The proposed approach retains the fault information in the axis trajectory to the greatest extent, and can adeptly extract and accurately identify features of various faults. The effectiveness and feasibility of the proposed methodology is demonstrated by using the sensed vibration signals collected from real-world centrifugal compressors and steam turbines with different fault modes.

show abstract

Classification of unbalance and misalignment in induction motors using orbital analysis and associative memories

Cited by 21 publications

References 35 publications

Vibration Analysis in Turbomachines Using Machine Learning Techniques

Vibration Analysis in Turbomachines Using Machine Learning Techniques

Linkboost: A Link Prediction Algorithm to Solve the Problem of Network Vulnerability in Cases Involving Incomplete Information

Novel Orbit-based CNN Model for Automatic Fault Identification of Rotating Machines

Contact Info

Product

Resources

About