scholarly journals On the Use of Machine Learning to Detect Shocks in Road Vehicle Vibration Signals

2016 ◽  
Vol 30 (8) ◽  
pp. 387-398 ◽  
Author(s):  
Julien Lepine ◽  
Vincent Rouillard ◽  
Michael Sek
Keyword(s):  
Machine Learning ◽  
Road Vehicle ◽  
Vibration Signals

scholarly journals Location Detection Method of Detector in Pipeline Using VMD Algorithm and Machine Learning Classifier

Electronics ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 1436
Author(s):  
Tuoru Li ◽  
Senxiang Lu ◽  
Enjie Xu
Keyword(s):  
Machine Learning ◽  
Magnetic Flux ◽  
Magnetic Flux Leakage ◽  
Intrinsic Mode Functions ◽  
Detection Range ◽  
Vibration Signals ◽  
Feature Vectors ◽  
Learning Classifier ◽  
Mode Decomposition ◽  
Flux Leakage

The internal detector in a pipeline needs to use the ground marker to record the elapsed time for accurate positioning. Most existing ground markers use the magnetic flux leakage testing principle to detect whether the internal detector passes. However, this paper uses the method of detecting vibration signals to track and locate the internal detector. The Variational Mode Decomposition (VMD) algorithm is used to extract features, which solves the defect of large noise and many disturbances of vibration signals. In this way, the detection range is expanded, and some non-magnetic flux leakage internal detectors can also be located. Firstly, the extracted vibration signals are denoised by the VMD algorithm, then kurtosis value and power value are extracted from the intrinsic mode functions (IMFs) to form feature vectors, and finally the feature vectors are input into random forest and Multilayer Perceptron (MLP) for classification. Experimental research shows that the method designed in this paper, which combines VMD with a machine learning classifier, can effectively use vibration signals to locate the internal detector and has the characteristics of high accuracy and good adaptability.

scholarly journals Application of FURIA for Finding the Faults in a Hydraulic Brake System Using a Vibration Analysis through a Machine Learning Approach

2019 ◽  
Vol 10 (1) ◽  
Author(s):  
Alamelu Manghai T. M ◽  
Jegadeeshwaran R
Keyword(s):  
Machine Learning ◽  
Fault Diagnosis ◽  
Decision Tree ◽  
Continuous Monitoring ◽  
Selection Process ◽  
Brake System ◽  
Learning Approach ◽  
Vibration Signals ◽  
Hydraulic Brake ◽  
Machine Learning Approach

Vibration-based continuous monitoring system for fault diagnosis of automobile hydraulic brake system is presented in this study. This study uses a machine learning approach for the fault diagnosis study. A hydraulic brake system test rig was fabricated. The vibration signals were acquired from the brake system under different simulated fault conditions using a piezoelectric transducer. The histogram features were extracted from the acquired vibration signals. The feature selection process was carried out using a decision tree. The selected features were classified using fuzzy unordered rule induction algorithm ( FURIA ) and Repeated Incremental Pruning to Produce Error Reduction ( RIPPER ) algorithm. The classification results of both algorithms for fault diagnosis of a hydraulic brake system were presented. Compared to RIPPER and J48 decision tree, the FURIA performs better and produced 98.73 % as the classification accuracy.

scholarly journals A Comparative Study for Condition Monitoring on Wind Turbine Blade using Vibration Signals through Statistical Features: a Lazy Learning Approach

2018 ◽  
Vol 7 (4.10) ◽  
pp. 190 ◽  
Author(s):  
A. Joshuva ◽  
V. Sugumaran
Keyword(s):  
Machine Learning ◽  
Wind Turbine ◽  
Phase 1 ◽  
Turbine Blades ◽  
Computational Time ◽  
Learning Approach ◽  
Statistical Features ◽  
Wind Turbine Blades ◽  
Vibration Signals ◽  
Machine Learning Approach

This study is to identify whether the wind turbine blades are in good or faulty conditions. If faulty, then the objective to find which fault condition are the blades subjected to. The problem identification is carried out by machine learning approach using vibration signals through statistical features. In this study, a three bladed wind turbine was chosen and faults like blade cracks, hub-blade loose connection, blade bend, pitch angle twist and blade erosion were considered. Here, the study is carried out in three phases namely, feature extraction, feature selection and feature classification. In phase 1, the required statistical features are extracted from the vibration signals which obtained from the wind turbine through accelerometer. In phase 2, the most dominating or the relevant feature is selected from the extracted features using J48 decision tree algorithm. In phase 3, the selected features are classified using machine learning classifiers namely, K-star (KS), locally weighted learning (LWL), nearest neighbour (NN), k-nearest neighbours (kNN), instance based K-nearest using log and Gaussian weight kernels (IBKLG) and lazy Bayesian rules classifier (LBRC). The results were compared with respect to the classification accuracy and the computational time of the classifier.  

scholarly journals Using Abductive Machine Learning for Online Vibration Monitoring of Turbo Molecular Pumps

1999 ◽  
Vol 6 (5-6) ◽  
pp. 253-265 ◽  
Author(s):  
R.E. Abdel-Aal ◽  
M. Raashid
Keyword(s):  
Machine Learning ◽  
Online Monitoring ◽  
Model Development ◽  
Low Frequency ◽  
Vibration Monitoring ◽  
Machine Learning Techniques ◽  
Vibration Signals ◽  
Output Performance ◽  
Learning Techniques ◽  
The Status

Turbo molecular vacuum pumps constitute a critical component in many accelerator installations, where failures can be costly in terms of both money and lost beam time. Catastrophic failures can be averted if prior warning is given through a continuous online monitoring scheme. This paper describes the use of modern machine learning techniques for online monitoring of the pump condition through the measurement and analysis of pump vibrations. Abductive machine learning is used for modeling the pump status as ‘good’ or ‘bad’ using both radial and axial vibration signals measured close to the pump bearing. Compared to other statistical methods and neural network techniques, this approach offers faster and highly automated model synthesis, requiring little or no user intervention. Normalized 50-channel spectra derived from the low frequency region (0–10 kHz) of the pump vibration spectra provided data inputs for model development. Models derived by training on only 10 observations predict the correct value of the logical pump status output with 100% accuracy for an evaluation population as large as 500 cases. Radial vibration signals lead to simpler models and smaller errors in the computed value of the status output. Performance is comparable with literature data on a similar diagnosis scheme for compressor valves using neural networks.

scholarly journals Failure analysis on a water pump based on a low-cost MEMS accelerometer and machine learning classifiers

2020 ◽  
Vol 41 (2) ◽  
pp. 171
Author(s):  
Luciane Agnoletti dos Santos Pedotti ◽  
Ricardo Mazza Zago ◽  
Jefferson Cutrim Rocha ◽  
José Gilberto Dalfré Filho ◽  
Mateus Giesbrecht ◽  
...  
Keyword(s):  
Machine Learning ◽  
Rotor Blade ◽  
Accurate Result ◽  
Low Cost ◽  
Failure Diagnosis ◽  
Support Vector ◽  
Water Pump ◽  
Vibration Signals ◽  
Mems Accelerometer ◽  
Machine Learning Classifiers

This work presents a failure diagnosis tool for a water pump using a low-cost MEMS accelerometer. It was inserted three types of failures: rotor blade (new and damaged), pump soleplate tightness (stiff or loose), and cavitation, in this case on three conditions: none, incipient and severe, totaling twelve fault combinations. These conditions were tested under two different speeds to perform the diagnosis, totaling twenty-four tests. In all cases, the vibration signals from axes X, Y, and Z were acquired. Some features extracted from the vibration spectra from X-axis were used to compose the dataset. These data were analyzed employing logistic regression, a linear support vector machine (SVM), and an artificial neural network multilayer perceptron (ANN-MLP). We compared these three techniques of machine learning and evaluated which one was able to obtain the most accurate result. Using the ANN-MLP, the system was able to detect all three types of failures inserted, with about 100% of accuracy on the rotor blade condition, 92% for anchorage faults, and about 99% accuracy on cavitation state. As a conclusion, it is demonstrated that this classifier algorithm can be used to process the data from the low-cost MEMS accelerometer in predictive maintenance as an accurate tool.

scholarly journals Vibrodiagnostics Used for Evaluating the Technical Condition of a Mechanical Gearbox

2018 ◽  
Vol 198 ◽  
pp. 02002
Author(s):  
Jan Furch ◽  
Jiří Stodola ◽  
Josef Glos
Keyword(s):  
Life Cycle ◽  
Root Mean Square ◽  
Technical Condition ◽  
The Other ◽  
Technical Diagnostics ◽  
Mean Square ◽  
Crest Factor ◽  
Road Vehicle ◽  
Vibration Signals ◽  
Fourth Position

The aim of this article is to evaluate the technical condition of a mechanical four-speed gearbox tightly connected to a mechanical two-speed auxiliary gearbox placed in an off-road vehicle. When observing the technical condition of the mechanical gearbox, we used one of technical diagnostics methods, namely vibrodiagnostics. The mechanical gearbox was monitored during all its life cycle up to failure occurrence. In the article there is a detailed description of the stand where the gearbox technical condition was monitored. When observing vibration signals, we used four tri-axial sensors placed in four selected spots depending on the design arrangement of antifriction bearings. In order to evaluate the measured results, two methods were applied, namely the root mean square gRMS based on the measured acceleration in three axes. The other method used for monitoring the technical condition was the Crest factor. The aim of the experiment was to monitor the technical condition of the mechanical gearbox, mainly its antifriction bearings, therefore the values were measured when the gear was put into fourth position and into overdrive in the auxiliary gearbox. When the gear is put into fourth position, the torque is transmitted from the motor through the main gearbox and the auxiliary gearbox directly. Single gearbox wheels are engaged but not loaded (they do not transmit the torque). At the end of our article we introduced the measured values along with the oral evaluation of the technical condition of the mechanical gearbox and the auxiliary gearbox.

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