L-Kurtosis and its application for fault detection of rolling element bearings

Liu, Shaopeng; Hou, S.; He, Kongde; Yang, Wei

doi:10.1016/j.measurement.2017.11.049

Cited by 58 publications

(25 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In any engineering field where data tends to be normal, kurtosis plays an important role in the detection of non-Gaussianity of the data. In the field of engineering maintenance, kurtosis acts as an indicator of signal impulsiveness, especially for rotary machinery vibration response [30][31]. Statistical measurement of kurtosis is expressed in eq (1):…”

Section: A Spectral Kurtosismentioning

confidence: 99%

An Effective Stator Fault Diagnosis Framework of BLDC Motor Based on Vibration and Current Signals

Shifat

Hur

2020

IEEE Access

View full text Add to dashboard Cite

Electric motor is a prominent rotary machinery in many engineering applications due to its excellent electrical energy utilization. With the increased demand in production and complex operating conditions, motors often run in a severe loading condition. Overload, overheating and many other intricate operating conditions account for the stator related faults in motors. Motor current signature analysis (MCSA) and vibration analysis have been popular techniques to diagnose different stator and rotor related faults in motors. But it is difficult to find the fault magnitude or fault threshold by using only one approach due to nonstationary motor operations. This paper presents a comprehensive review of a permanent magnet brushless DC motor's (BLDC motor) fault diagnosis combining vibration and current signals collected from sensors. Since the insulation break in the stator winding is the most commonly occurring fault in the stator, a short-circuit was artificially created between two windings. Based on the motor operating conditions, three health states are chosen from the experimental sensor data with different fault magnitudes. Health states are labeled as healthy state, incipient failure state, and severe failure state. Two effective fault diagnosis indices named kurtosis and third harmonic of motor current are selected for analyzing the vibration signals and current signals, respectively. Proposed diagnostics framework is validated using experimental data and proven to detect the stator fault at the early stage as well as distinguish between different fault states. Monitoring both mechanical and electrical characteristics of BLDC motor provides a thorough understanding of fault magnitude and threshold in different health states.

show abstract

Section: A Spectral Kurtosismentioning

confidence: 99%

An Effective Stator Fault Diagnosis Framework of BLDC Motor Based on Vibration and Current Signals

Shifat

Hur

2020

IEEE Access

View full text Add to dashboard Cite

show abstract

“…As an effective indicator used in fault detection, L-Kurtosis can give a more correct parameter estimates than kurtosis. In this paper, L-Kurtosis is introduced to select the optimal IMF which contains the faulty information [26].…”

Section: B L-kurtosismentioning

confidence: 99%

“…u q:q is the corresponding order statistics, respectively. The rth L-moment η r [26] can be defined as:…”

Section: B L-kurtosismentioning

confidence: 99%

“…However, the performance of kurtosis is greatly limited by its intrinsic drawback which makes it very susceptible to the outliers. Compared to kurtosis, L-Kurtosis [26] gives good impulse recognition performance while overcoming the drawback of kurtosis. For faulty bearing and gear, the IMF corresponding to the maximum L-Kurtosis value might be the optimal signal which contains the faulty information.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Strategy Using Variational Mode Decomposition, L-Kurtosis and Minimum Entropy Deconvolution to Detect Mechanical Faults

Liu

Xiang

2019

IEEE Access

View full text Add to dashboard Cite

When faults occur in mechanical components, the faulty information is usually manifested as a series of periodic impulses which correspond to the faulty feature frequencies. However, due to the nonstationary characteristic of the raw vibration signals, the faulty feature frequencies are difficult extracted. In this paper, a novel strategy using variational mode decomposition (VMD), L-Kurtosis and minimum entropy deconvolution (MED) is proposed to detect mechanical faults. First, VMD is employed to decompose the raw vibration signal into a set of intrinsic mode functions (IMFs) to eliminate the interference of the noise. Second, the optimal intrinsic mode function (IMF) which contains the faulty information is determined using L-Kurtosis. Then, the impact characteristic of the periodic impulses in optimal IMF is enhanced through MED. Finally, a Hilbert envelope spectrum analysis is performed to the enhanced signal to extract the faulty feature frequency. In order to illustrate the performance of the proposed strategy, the simulation signal and real experimental signals collected from faulty rolling element bearings and gears are analyzed. The results show that the strategy using the VMD, L-Kurtosis, and MED can detect mechanical component faults effectively.

show abstract

“…The TE process is unstable in open-loop mode and the first limitation that causes this instability is exceeding from reactor allowed pressure range. Realized control structure should be able to save process in operational conditions and can put process variables in its proposed constraints range [4][5][6][7][8][9][10][11][12][13][14][15][16][17]. Regarding the proposed research novelty and its innovation, it can be expressed in three lines.…”

Section: Introductionmentioning

confidence: 99%

High efficiency fault-detection and fault-tolerant control approach in Tennessee Eastman process via fuzzy-based neural network representation

Adeli

Mazinan

2019

Complex Intell. Syst.

View full text Add to dashboard Cite

We looked at the background of fault-detection and fault-tolerant control algorithms to propose a new high efficiency one with a focus on Tennessee Eastman process through fuzzy-based neural network representation. Due to the fact that the openloop system may not be stabilized, an advanced control strategy to generate proper control signals needs to be designed. At first, to detect and identify the fault, data preprocessing theories have been considered. Based upon the matter disclosed, to provide a reliable decision-maker block, fusion classifier idea has been realized. For this one, raw data, time, and frequency characteristics are divided into various classification tools and finally the obtained knowledge combination regarding each one of them is adopted. It should be noted that the proposed implementation tools are taken into real consideration as the fuzzy-based neural network representation. Subsequently, the fault-tolerant control approach based on local controller regulation in case of each fault occurrence has been researched, which the investigated outcomes emphasize the effectiveness of the approach proposed here.

show abstract

L-Kurtosis and its application for fault detection of rolling element bearings

Cited by 58 publications

References 26 publications

An Effective Stator Fault Diagnosis Framework of BLDC Motor Based on Vibration and Current Signals

An Effective Stator Fault Diagnosis Framework of BLDC Motor Based on Vibration and Current Signals

A Strategy Using Variational Mode Decomposition, L-Kurtosis and Minimum Entropy Deconvolution to Detect Mechanical Faults

High efficiency fault-detection and fault-tolerant control approach in Tennessee Eastman process via fuzzy-based neural network representation

Contact Info

Product

Resources

About