scholarly journals Gradual Wear Diagnosis of Outer-Race Rolling Bearing Faults through Artificial Intelligence Methods and Stray Flux Signals

Electronics ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 1486
Author(s):  
Israel Zamudio-Ramirez ◽  
Roque A. Osornio-Rios ◽  
Jose A. Antonino-Daviu ◽  
Jonathan Cureño-Osornio ◽  
Juan-Jose Saucedo-Dorantes
Keyword(s):  
Kinematic Chain ◽  
Industrial Applications ◽  
Electric Motors ◽  
Feed Forward Neural Network ◽  
Mechatronic Systems ◽  
Linear Discriminant ◽  
Bearing Faults ◽  
Outer Race ◽  
Non Invasive ◽  
Operation Conditions

Electric motors have been widely used as fundamental elements for driving kinematic chains on mechatronic systems, which are very important components for the proper operation of several industrial applications. Although electric motors are very robust and efficient machines, they are prone to suffer from different faults. One of the most frequent causes of failure is due to a degradation on the bearings. This fault has commonly been diagnosed at advanced stages by means of vibration and current signals. Since low-amplitude fault-related signals are typically obtained, the diagnosis of faults at incipient stages turns out to be a challenging task. In this context, it is desired to develop non-invasive techniques able to diagnose bearing faults at early stages, enabling to achieve adequate maintenance actions. This paper presents a non-invasive gradual wear diagnosis method for bearing outer-race faults. The proposal relies on the application of a linear discriminant analysis (LDA) to statistical and Katz’s fractal dimension features obtained from stray flux signals, and then an automatic classification is performed by means of a feed-forward neural network (FFNN). The results obtained demonstrates the effectiveness of the proposed method, which is validated on a kinematic chain (composed by a 0.746 KW induction motor, a belt and pulleys transmission system and an alternator as a load) under several operation conditions: healthy condition, 1 mm, 2 mm, 3 mm, 4 mm, and 5 mm hole diameter on the bearing outer race, and 60 Hz, 50 Hz, 15 Hz and 5 Hz power supply frequencies

Non-invasive temperature measurement of turbulent flows of aqueous solutions and gases in pipes

2020 ◽  
Vol 87 (9) ◽  
pp. 553-563
Author(s):  
Jörg Gebhardt ◽  
Guruprasad Sosale ◽  
Subhashish Dasgupta
Keyword(s):  
Surface Temperature ◽  
Temperature Measurement ◽  
Turbulent Flows ◽  
Fluid Dynamic ◽  
Industrial Applications ◽  
Static Case ◽  
Pipe Surface ◽  
Non Invasive ◽  
Step Model ◽  
Plant Optimization

AbstractAccurate and responsive non-invasive temperature measurements are enablers for process monitoring and plant optimization use cases in the context of Industry 4.0. If their performance is proven for large classes of applications, such measurement principles can replace traditional invasive measurements. In this paper we describe a two-step model to estimate the process temperature from a pipe surface temperature measurement. This static case model is compared to and enhanced by computational fluid dynamic (CFD) calculations to predict transient situations. The predictions of the approach are validated by means of controlled experiments in a laboratory environment. The experimental results demonstrate the efficacy of the model, the responsiveness of the pipe surface temperature, and that state of the art industrial non-invasive sensors can achieve the performance of invasive thermowells. The non-invasive sensors are then used to demonstrate the performance of the model in industrial applications for cooling fluids and steam.

scholarly journals Diagnostic Performance of Oncuria™, a Urinalysis Test for Bladder Cancer

2021 ◽  
Author(s):  
Yosuke Hirasawa ◽  
Ian Pagano ◽  
Runpu Chen ◽  
Yijun Sun ◽  
Yunfeng Dai ◽  
...  
Keyword(s):  
Bladder Cancer ◽  
Diagnostic Performance ◽  
Clinical Laboratory ◽  
Demographic Data ◽  
Roc Curves ◽  
Low Grade ◽  
Linear Discriminant ◽  
Non Invasive ◽  
Diagnostic Panel ◽  
Parallel Measurement

Abstract Background: Due to insufficient accuracy, urine-based assays currently have a limited role in the management of patients with bladder cancer. The identification of multiplex molecular signatures associated with disease has the potential to address this deficiency and to assist with accurate, non-invasive diagnosis and monitoring. Methods: To evaluate the performance of Oncuria™, a multiplex immunoassay for bladder detection in voided urine samples. The test was evaluated in a multi-institutional cohort of 362 prospectively collected subjects presenting for bladder cancer evaluation. The parallel measurement of 10 biomarkers (A1AT, APOE, ANG, CA9, IL8, MMP9, MMP10, PAI1, SDC1 and VEGFA) was performed in an independent clinical laboratory. The ability of the test to identify patients harboring bladder cancer was assessed. Bladder cancer status was confirmed by cystoscopy and tissue biopsy. The association of biomarkers and demographic factors was evaluated using linear discriminant analysis (LDA) and predictive models were derived using supervised learning and cross-validation analyses. Diagnostic performance was assessed using ROC curves.Results: The combination of the 10 biomarkers provided an AUROC 0.93 [95% CI: 0.87 – 0.98], outperforming any single biomarker. The addition of demographic data (age, sex, and race) into a hybrid signature improved the diagnostic performance AUROC 0.95 [95% CI: 0.90 – 1.00]. The hybrid signature achieved an overall sensitivity of 0.93, specificity of 0.93, PPV of 0.65 and NPV of 0.99 for bladder cancer classification. Sensitivity values of the diagnostic panel for high-grade bladder cancer, low-grade bladder cancer, MIBC and NMIBC were 0.94, 0.89, 0.97 and 0.93, respectively. Conclusions: Urinary levels of a biomarker panel enabled the accurate discrimination of bladder cancer patients and controls. The multiplex Oncuria™ test can achieve the efficient and accurate detection and monitoring of bladder cancer in a non-invasive patient setting.

scholarly journals An Improved Method Based on CEEMD for Fault Diagnosis of Rolling Bearing

2014 ◽  
Vol 6 ◽  
pp. 676205 ◽  
Author(s):  
Meijiao Li ◽  
Huaqing Wang ◽  
Gang Tang ◽  
Hongfang Yuan ◽  
Yang Yang
Keyword(s):  
Fault Diagnosis ◽  
Empirical Mode Decomposition ◽  
Early Stage ◽  
Rolling Bearing ◽  
Ensemble Empirical Mode Decomposition ◽  
Rolling Element Bearing ◽  
Bearing Faults ◽  
Outer Race ◽  
Mode Decomposition ◽  
Acceleration Sensors

In order to improve the effectiveness for identifying rolling bearing faults at an early stage, the present paper proposed a method that combined the so-called complementary ensemble empirical mode decomposition (CEEMD) method with a correlation theory for fault diagnosis of rolling element bearing. The cross-correlation coefficient between the original signal and each intrinsic mode function (IMF) was calculated in order to reduce noise and select an effective IMF. Using the present method, a rolling bearing fault experiment with vibration signals measured by acceleration sensors was carried out, and bearing inner race and outer race defect at a varying rotating speed with different degrees of defect were analyzed. And the proposed method was compared with several algorithms of empirical mode decomposition (EMD) to verify its effectiveness. Experimental results showed that the proposed method was available for detecting the bearing faults and able to detect the fault at an early stage. It has higher computational efficiency and is capable of overcoming modal mixing and aliasing. Therefore, the proposed method is more suitable for rolling bearing diagnosis.

Tool-Based Approach for the Develepment of Self-Optimizing Systems With Solution Patterns

2010 ◽  
Author(s):  
Roman Dumitrescu ◽  
Ju¨rgen Gausemeier ◽  
Sascha Kahl
Keyword(s):  
Information Technology ◽  
Mathematical Optimization ◽  
Work Flow ◽  
Mechatronic Systems ◽  
Control Engineering ◽  
Effective Work ◽  
Domain Specific ◽  
Operation Conditions ◽  
Software Engineers ◽  
And Storage

Machines are omnipresent. They produce, they transport. Machines facilitate work and assist. The increasing penetration of mechanical engineering by information technology enables considerable benefits. This circumstance is expressed by the term mechatronics, which means the close interaction of mechanics, electronics, control engineering and software engineering to improve the behavior of a technical system. The progressive integration of information technology will enable mechatronic systems with partial intelligence. We refer to such systems as self-optimizing systems. Self-optimizing systems have the ability to react autonomously and flexibly on changing operation conditions. The design of such systems is an even more interdisciplinary task than the design of conventional mechatronic systems. Additionally to mechanical, electrical, control and software engineers also experts from mathematical optimization and artificial intelligence are involved. As a consequence a domain-spanning methodology is necessary in order to guarantee an effective work flow between the participating developers from various domains and their domain-specific methods, terminologies and solutions. This contribution presents such a methodology. The main focus, however, lies on harnessing of experimental knowledge for the development of self-optimizing systems. This includes the generation and storage of once proven design solutions as well as a tool for the effective and domain-spanning reuse.

scholarly journals Bearing Health Monitoring Using Relief-F-Based Feature Relevance Analysis and HMM

2020 ◽  
Vol 10 (15) ◽  
pp. 5170
Author(s):  
José Alberto Hernández-Muriel ◽  
Jhon Bryan Bermeo-Ulloa ◽  
Mauricio Holguin-Londoño ◽  
Andrés Marino Álvarez-Meza ◽  
Álvaro Angel Orozco-Gutiérrez
Keyword(s):  
Feature Selection ◽  
Energy Demand ◽  
Feature Selection Method ◽  
Electric Motors ◽  
Invasive Approach ◽  
Time Frequency ◽  
Non Invasive ◽  
Knowledge Based ◽  
Feature Relevance ◽  
Stochastic Feature

Nowadays, bearings installed in industrial electric motors are constituted as the primary mode of a failure affecting the global energy consumption. Since industries’ energy demand has a growing tendency, interest for efficient maintenance in electric motors is decisive. Vibration signals from bearings are employed commonly as a non-invasive approach to support fault diagnosis and severity evaluation of rotating machinery. However, vibration-based diagnosis poses a challenge concerning the signal properties, e.g., highly dynamic and non-stationary. Here, we introduce a knowledge-based tool to analyze multiple health conditions in bearings. Our approach includes a stochastic feature selection method, termed Stochastic Feature Selection (SFS), highlighting and interpreting relevant multi-domain attributes (time, frequency, and time–frequency) related to the bearing faults discriminability. In particular, a relief-F-based ranking and a Hidden Markov Model are trained under a windowing scheme to achieve our SFS. Obtained results in a public database demonstrate that our proposal is competitive compared to state-of-the-art algorithms concerning both the number of features selected and the classification accuracy.

PREDICTION OF POTENTIAL HIT SONG AND MUSICAL GENRE USING ARTIFICIAL NEURAL NETWORKS

2009 ◽  
Vol 20 (11) ◽  
pp. 1697-1718 ◽  
Author(s):  
CHRISTOPHER MONTEROLA ◽  
CHERYL ABUNDO ◽  
JERIC TUGAFF ◽  
LORCEL ERICKA VENTURINA
Keyword(s):  
Neural Network ◽  
Feed Forward Neural Network ◽  
Linear Discriminant ◽  
The Public ◽  
Musical Genre ◽  
Alternative Rock ◽  
Discriminating Power ◽  
Average Accuracy ◽  
Classification And Regression ◽  
Size Criterion

Accurately quantifying the goodness of music based on the seemingly subjective taste of the public is a multi-million industry. Recording companies can make sound decisions on which songs or artists to prioritize if accurate forecasting is achieved. We extract 56 single-valued musical features (e.g. pitch and tempo) from 380 Original Pilipino Music (OPM) songs (190 are hit songs) released from 2004 to 2006. Based on an effect size criterion which measures a variable's discriminating power, the 20 highest ranked features are fed to a classifier tasked to predict hit songs. We show that regardless of musical genre, a trained feed-forward neural network (NN) can predict potential hit songs with an average accuracy of Φ NN = 81%. The accuracy is about +20% higher than those of standard classifiers such as linear discriminant analysis (LDA, Φ LDA = 61%) and classification and regression trees (CART, Φ CART = 57%). Both LDA and CART are above the proportional chance criterion (PCC, Φ PCC = 50%) but are slightly below the suggested acceptable classifier requirement of 1.25*Φ PCC = 63%. Utilizing a similar procedure, we demonstrate that different genres (ballad, alternative rock or rock) of OPM songs can be automatically classified with near perfect accuracy using LDA or NN but only around 77% using CART.

scholarly journals Empirical Mode Decomposition and Neural Networks on FPGA for Fault Diagnosis in Induction Motors

2014 ◽  
Vol 2014 ◽  
pp. 1-17 ◽  
Author(s):  
David Camarena-Martinez ◽  
Martin Valtierra-Rodriguez ◽  
Arturo Garcia-Perez ◽  
Roque Alfredo Osornio-Rios ◽  
Rene de Jesus Romero-Troncoso
Keyword(s):  
Empirical Mode Decomposition ◽  
High Performance ◽  
Induction Motors ◽  
Industrial Applications ◽  
Feed Forward Neural Network ◽  
Linear Network ◽  
Multiple Faults ◽  
Online Systems ◽  
Time Operation ◽  
Mode Decomposition

Nowadays, many industrial applications require online systems that combine several processing techniques in order to offer solutions to complex problems as the case of detection and classification of multiple faults in induction motors. In this work, a novel digital structure to implement the empirical mode decomposition (EMD) for processing nonstationary and nonlinear signals using the full spline-cubic function is presented; besides, it is combined with an adaptive linear network (ADALINE)-based frequency estimator and a feed forward neural network (FFNN)-based classifier to provide an intelligent methodology for the automatic diagnosis during the startup transient of motor faults such as: one and two broken rotor bars, bearing defects, and unbalance. Moreover, the overall methodology implementation into a field-programmable gate array (FPGA) allows an online and real-time operation, thanks to its parallelism and high-performance capabilities as a system-on-a-chip (SoC) solution. The detection and classification results show the effectiveness of the proposed fused techniques; besides, the high precision and minimum resource usage of the developed digital structures make them a suitable and low-cost solution for this and many other industrial applications.

A heterogeneous fault diagnosis method for bearings in gearbox

2014 ◽  
Vol 229 (8) ◽  
pp. 1491-1499 ◽  
Author(s):  
Xiaohui Chen ◽  
Lei Xiao ◽  
Xinghui Zhang ◽  
Zhenxiang Liu
Keyword(s):  
Fault Diagnosis ◽  
Bearing Failure ◽  
Optimum Frequency ◽  
Bearing Faults ◽  
Outer Race ◽  
Bearing Fault ◽  
Spectral Kurtosis ◽  
Bearing Fault Diagnosis ◽  
Diagnosis Method ◽  
Inner Race

Bearing failure is one of the most important causes of breakdown of rotating machinery. These failures can lead to catastrophic disasters or result in costly downtime. One of the key problems in bearing fault diagnosis is to detect the bearing fault as early as possible. This capability enables the operator to have enough time to do some preventive maintenance. Most papers investigate the bearing faults under rational assumption that bearings work individually. However, bearings are usually working as a part of complex systems like a gearbox. The fault signal of bearings can be easily masked by other vibration generated from gears and shafts. The proposed method separates bearing signals from other signals, and then the optimum frequency band which the bearing fault signal is prominent is determined by mean envelope Kurtosis. Subsequently, the envelope analysis is used to detect the bearing faults. Finally, two bearing fault experiments are used to validate the proposed method. Each experiment contains two bearing fault modes, inner race fault and outer race fault. The results demonstrate that the proposed method can detect the bearing fault easier than spectral Kurtosis and envelope Kurtosis.

scholarly journals Analysis of classification methods suitable for band limited spatially filtered EEG signal applicable to non-invasive BCI

2019 ◽  
Vol 8 (9) ◽  
pp. 1689-1694
Keyword(s):  
Feature Extraction ◽  
Nearest Neighbor ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Common Spatial Pattern ◽  
Linear Discriminant ◽  
Feature Extraction Method ◽  
Non Invasive ◽  
Band Limited ◽  
Ca Sensitivity

Electroencephalographic (EEG) signals are the preferred input for non-invasive Brain-Computer Interface (BCI). Efficient signal processing strategies, including feature extraction and classification, are required to distinguish the underlying task of BCI. This work proposes the optimized common spatial pattern(CSP) filtering technique as the feature extraction method for collecting the spatially spread variation of the signal. The bandpass filter (BPF) designed for this work assures the availability of event-related synchronized (ERS) and event-related desynchronized (ERD) signal as input to the spatial filter. This work takes consideration of the area-specific electrodes for feature formation. This work further proposes a comparative analysis of classifier algorithms for classification accuracy(CA), sensitivity and specificity and the considered algorithms are Support Vector Machine(SVM), Linear Discriminant Analysis(LDA), and K-Nearest Neighbor(KNN). Performance parameters considered are CA, sensitivity, and selectivity, which can judge the method not only for high CA but also inclining towards the particular class. Thus it will direct in the selection of appropriate classifier as well as tuning the classifier to get the balanced results. In this work, CA, the prior performance parameter is obtained to be 88.2% sensitivity of 94.2% and selectivity 82.2% for the cosine KNN classifier. SVM with linear kernel function also gives the comparable results, thus concluding that the robust classifiers perform well for all parameters in case of CSP for feature extraction.

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