Detection of inappropriate working conditions for the timing belt in internal-combustion engines using vibration signals and data mining

2016 ◽  
Vol 231 (3) ◽  
pp. 418-432 ◽  
Author(s):  
Meghdad Khazaee ◽  
Ahmad Banakar ◽  
Barat Ghobadian ◽  
Mostafa Agha Mirsalim ◽  
Saeid Minaei ◽  
...  
Keyword(s):  
High Temperature ◽  
Frequency Domain ◽  
High Load ◽  
Operating Conditions ◽  
Transform Method ◽  
Time Frequency ◽  
Normal Operating ◽  
Timing Belt ◽  
Temperature Operating ◽  
Abnormal Operating Conditions

Abnormal operating conditions for the timing belt can lead to cracks, fatigue, sudden rupture and damage to engines. In this study, an intelligent system was developed to detect and classify high-load operating conditions and high-temperature operating conditions for timing belts. To achieve this, vibration signals in normal operating conditions, high-load operating conditions and high-temperature operating conditions were collected. Time-domain signals were transformed to the frequency domain and the time–frequency domain using the fast Fourier transform method and the wavelet transform method respectively. In the data-mining stage, 25 statistical features were extracted from different signal domains. The improved distance evaluation method was adopted to select the best features and to reduce the input space for the classifier. Then, the signal features from the time domain, the frequency domain and the time-frequency domain were fed into an artificial neural network to evaluate the accuracy of this designed procedure for detecting inappropriate operating conditions for the timing belt. Based on all these features extracted from the signals in the time, frequency and time–frequency domains, the artificial neural network classifier detected and classified normal operating conditions, high-load operating conditions and high-temperature operating conditions with accuracies of 73.3%, 85% and 89.2% respectively. The classification accuracies using features selected by improved distance evaluation in the signals from the time, frequency and time–frequency domains were found to be 85%, 95.8% and 95% respectively. The results showed that the developed system was capable of detecting and classifying both the normal operating conditions and abnormal operating conditions for the timing belt. The results also suggested that a combination of signal processing and feature selection can significantly enhance the classification accuracy.

scholarly journals Improvement of Intake Structures in a Two-Way Pumping Station with Experimental Analysis

2020 ◽  
Vol 10 (19) ◽  
pp. 6842
Author(s):  
Yanjun Li ◽  
Rong Lu ◽  
Huiyan Zhang ◽  
Fanjie Deng ◽  
Jianping Yuan
Keyword(s):  
Frequency Domain ◽  
Great Influence ◽  
Operating Conditions ◽  
Stable Operation ◽  
Pressure Pulsations ◽  
Pump Unit ◽  
Measuring Point ◽  
Time Frequency ◽  
Pumping Station ◽  
Bell Mouth

Pumping stations are important regulation facilities in a water distribution system. Intake structures can generally have a great influence on the operational state of the pumping station. To analyze the effects of the bell mouth height of the two-way intake on the performance characteristics and the pressure pulsations of a two-way pumping station, the laboratory-sized model pump units with three different intakes were experimentally investigated. To facilitate parameterized control, ellipse and straight lines were used to construct the profile of the bell mouth. The frequency domain and time-frequency domain of the pressure pulsations on the wall of intakes were analyzed by the Welch’s power spectral density estimate and the continuous wavelet transform (CWT) methods, respectively. The results showed that the bell mouth height (H) has significant influences on the uniformity of the impeller inflow and the operation stability of the pump unit. When H = 204 mm, the data fluctuated greatly throughout the test process and the performance curves are slightly lower than the other two schemes. As the bell mouth height gradually decreases, the average pressure difference of each measuring point began to decrease, more homogeneous velocity distribution of impeller inflow can be ensured. The amplitude of blade passing frequency is obvious in the spectrum. While when (H) is more than 164 mm, the main frequency of pressure pulsations at three points fluctuates with the rotation of the impeller. When H decreases to 142 mm, pressure pulsations will be independent of the operating conditions and positions which contributes to the long-term stable operation of the pump unit.

Research of Aircraft Cable Fault Detecting Method Based on Hilbert Huang Transformation

2011 ◽  
Vol 214 ◽  
pp. 138-143
Author(s):  
Tao Jing ◽  
Lu Zhang ◽  
Xu Dong Shi ◽  
Li Wen Wang
Keyword(s):  
Frequency Domain ◽  
Short Circuit ◽  
Reference Signal ◽  
Open Circuit ◽  
Time Domain Reflectometry ◽  
Transform Method ◽  
Hilbert Huang Transform ◽  
Intermittent Faults ◽  
Time Frequency ◽  
Frequency Domain Reflectometry

Aircraft cable fault diagnosing is considered to be most important for engineering maintenance. Several methods for cables testing have been developed, such as TDR, FDR and TFDR. Time Domain Reflectometry (TDR) relays much on impedance changes on the fault position, which is hard to using in detecting high resistance defects, intermittent defects; Time Frequency Domain Reflectometry (TFDR) method is used to locate intermittent faults, continuous faults and cross-connection faults aircraft wire, however, the algorithm of TFDR is complex. To the "Hard Fault"(short circuit and open circuit), the Hilbert-Huang Transform method is used in determining the optimal bandwidth of the incident reference signal and analyzing the phase and amplitude difference of superimposed signal which from the incident signal and the reflected signal on defects. To the "Fray Fault", Time and Frequency Domain Reflectometry method can be used with the signal processing method with Hilbert-Huang Transform. The experimental results indicate that this method effectively detect all types of aircraft cable fault, particularly for short lengths of cable.

Class-information–incorporated kernel entropy component analysis with application to bearing fault diagnosis

2020 ◽  
pp. 107754632093203
Author(s):  
Hongdi Zhou ◽  
Fei Zhong ◽  
Tielin Shi ◽  
Wuxing Lai ◽  
Jian Duan ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Frequency Domain ◽  
Hebbian Learning ◽  
Component Analysis ◽  
Operating Conditions ◽  
Time Frequency ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Class Information ◽  
Entropy Component

Rolling bearings are present ubiquitously in industrial fields; timely fault diagnosis is of crucial significance in avoiding serious catastrophe. The extraction of ideal fault feature is a challenging task in vibration-based bearing fault detection. In this article, a novel method called class-information–incorporated kernel entropy component analysis is proposed for bearing fault diagnosis. The method is developed based on the Hebbian learning theory of neural network and the kernel entropy component analysis which attempts to compress the most Renyi quadratic entropy of input dataset after dimension reduction and presents a good performance for nonlinear feature extraction. Class-information–incorporated kernel entropy component analysis can take advantage of the label information of training samples to guide dimensional reduction and still follow the same simple mathematical formulation as kernel entropy component analysis. The high-dimensional feature dataset including time-domain, frequency-domain, and time–frequency domain characteristic parameters is first derived from the vibration signals. Then, the intrinsic geometric features are extracted by class-information–incorporated kernel entropy component analysis, and a classification strategy based on fusion information is applied to recognize different operating conditions of bearings. The experimental results demonstrated the feasibility and effectiveness of the proposed method.

scholarly journals Application of the Acoustic Emission Method for Diagnosis of On-Load Tap Changer

2017 ◽  
Vol 42 (1) ◽  
pp. 29-35 ◽  
Author(s):  
Henryk Majchrzak ◽  
Andrzej Cichoń ◽  
Sebastian Borucki
Keyword(s):  
Acoustic Emission ◽  
Frequency Domain ◽  
Time Domain ◽  
Operating Conditions ◽  
Time Frequency ◽  
Measurement Results ◽  
Tap Changer ◽  
The Time Domain ◽  
Load Tap Changer ◽  
Short Time

Abstract This paper provides an example of the application of the acoustic emission (AE) method for the diagnosis of technical conditions of a three-phase on-load tap-changer (OLTC) GIII type. The measurements were performed for an amount of 10 items of OLTCs, installed in power transformers with a capacity of 250 MVA. The study was conducted in two different OLTC operating conditions during the tapping process: under load and free running conditions. The analysis of the measurement results was made in both time domain and time-frequency domain. The description of the AE signals generated by the OLTC in the time domain was performed using the analysis of waveforms and determined characteristic times. Within the time-frequency domain the measured signals were described by short-time Fourier transform spectrograms.

scholarly journals Effects of high-temperature operating conditions on the through-plane gas permeability of gas diffusion layers used in PEFCs

2021 ◽  
Vol 1 (1) ◽  
Author(s):  
Raja Muhammad Aslam Raja Arif ◽  
Keyword(s):  
High Temperature ◽  
Gas Permeability ◽  
Gas Diffusion ◽  
Temperature Treatment ◽  
Operating Conditions ◽  
Sem Images ◽  
Gas Diffusion Layers ◽  
Diffusion Layers ◽  
Temperature Operating ◽  
Increasing Temperature

In order to simulate the effects of high-temperature operating conditions on the through-plane gas permeability of gas diffusion layers (GDLs) used in polymer electrolyte fuel cells, uncoated and coated GDLs were heated at various temperatures (i.e. 200, 500 and 800 °C). The results show that the through-plane gas permeability of the uncoated GDLs generally increases after higher temperature treatment. However, the coated GDL displays a different trend: the through-plane gas permeability increases with increasing temperature treatment to 200 and 500 °C, but then decreases after heat treatment at 800 and 1000 °C. With the assistance of SEM images, the above results are discussed.

Diagnosis of the Joint Backlash of a Mechanism: Wigner-Ville Distribution Combined With Correlation Techniques

1995 ◽  
Author(s):  
M.-C. Pan ◽  
B. Verbeure ◽  
H. Van Brussel ◽  
P. Sas
Keyword(s):  
Signal Detection ◽  
Operating Conditions ◽  
Degree Of Freedom ◽  
Two Dimensional ◽  
Detection Problem ◽  
Time Frequency ◽  
Normal Operating ◽  
Link Mechanism ◽  
Pattern Differentiation ◽  
Correlation Techniques

Abstract The aim of this paper is to develop appropriate techniques to detect and classify the joint backlash of a robot by monitoring its vibration response during normal operating conditions. In this investigation, Wigner-Ville distributions combined with two-dimensional correlation techniques have been employed to diagnose various degrees of single-joint backlash. The method also allows to detect and to single out backlash present in two joints of a multi-link mechanism. In the work reported here, the Wigner-Ville distribution based signal detection and the generalized symmetrical Itakura distance were proposed as tools for pattern differentiation. Initially the proposed methods have been applied to quantify the backlash of a single joint. Consecutively, the detection problem has been generalized to diagnose faults in two joints simultaneously. Due to the extra degree of freedom given by the 2D nature of the WVD’s, certain time-frequency regions were chosen as reference signatures in the case of single-joint backlash, and the signatures, spanning over two impact transients at the reverses of motion, were chosen in the cases of double-joint backlash. The proposed techniques have been successfully implemented on a two-link mechanism.

scholarly journals Application of Cyclo-Non-Stationary Indicators for Bearing Monitoring Under Varying Operating Conditions

2017 ◽  
Author(s):  
Konstantinos Gryllias ◽  
Simona Moschini ◽  
Jerome Antoni
Keyword(s):  
Frequency Domain ◽  
Condition Monitoring ◽  
Health Monitoring ◽  
Computational Cost ◽  
Rotating Machinery ◽  
Operating Conditions ◽  
Vibration Monitoring ◽  
Cyclic Behavior ◽  
Diagnostic Features ◽  
Time Frequency

Condition monitoring assesses the operational health of rotating machinery, in order to provide early and accurate warning of potential failures such that preventative maintenance actions may be taken. To achieve this target, manufacturers start taking on the responsibilities of engine condition monitoring, by embedding health monitoring systems within each engine unit and prompting maintenance actions when necessary. Several types of condition monitoring are used including oil debris monitoring, temperature monitoring and vibration monitoring. Among them, vibration monitoring is the most widely used technique. Machine vibro-acoustic signatures contain pivotal information about its state of health. The current work focuses on one part of the diagnosis stage of condition monitoring for engine bearing health monitoring as bearings are critical components in rotating machinery. A plethora of signal processing tools and methods applied at the time domain, the frequency domain, the time-frequency domain and the time-scale domain have been presented in order to extract valuable information by proposing different diagnostic features. Among others, an emerging interest has been reported on modeling rotating machinery signals as cyclostationary, which is a particular class of non-stationary stochastic processes. A process x(t) is said to be nth-order cyclostationary with period T if its nth-order moments exist and are periodic with period T. Several tools, such as the Spectral Correlation Density (SCD) and the Cyclic Modulation Spectrum (CMS) can be used in order to extract interesting information concerning the cyclic behavior of cyclostationary signals. In order to measure the cyclostationarity from order 1 to 4, concise and global indicators have been proposed. However, in a number of applications such as aircraft engines and wind turbines the characteristic vibroacoustic signatures of rotating machinery depend on the operating conditions of the rotational speed and/or the load. During the last decades fault diagnostics of rotating machinery under variable speed/load has attracted a lot of interest. The classical cyclostationary tools can be used under the assumption that the speed of machinery is constant or nearly constant, otherwise the vibroacoustic signal becomes cyclo-non-stationary. In order to overcome this limitation a generalization of both SCD and CMS functions have been proposed displaying cyclic Order versus Frequency. The goal of this paper is to propose a novel approach for the analysis of cyclo-nonstationary signals based on the generalization of indicators of cyclostationarity in order to cover the speed varying conditions. The proposed indicators of cyclo-non-stationarity (ICNS) are expected to summarize the information at various statistical orders and at lower computational cost compared to the Order-Frequency SCD or CMS. This generalization is realized by introducing a new speed-dependent angle averaging operator. The effectiveness of the approach is evaluated on an acceleration signal captured on the casing of an aircraft engine gearbox, provided by SAFRAN, in the frames of SAFRAN contest which took place at the Surveillance 8 International Conference.

Lubricating Characteristics of Fluorine Grease Containing Silica Nanoparticles

2008 ◽  
Author(s):  
Michita Hokao ◽  
Atsushi Yokouchi
Keyword(s):  
High Temperature ◽  
Service Life ◽  
Silica Nanoparticles ◽  
Operating Conditions ◽  
Automotive Control ◽  
Control Units ◽  
Temperature Operating

Electrical motor bearings for use in automotive control units are sometimes exposed to high-temperature operating conditions. Therefore, these bearings are sometimes packed with fluorine grease in order to meet the demand for long service life performance. In recent years, there is growing demand for lower initial torque in these same bearings without sacrificing service life performance. This paper describes the effects of fluorine grease containing silica nanoparticles on initial bearing torque of electrical motor bearings. Silica nanoparticles are effective in helping to reduce amounts of initial bearing torque and help stabilize bearing torque due to improved channeling characteristics.

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