Spark plug failure detection using Z-freq and machine learning

This paper presents the development of a methodology to detect and evaluate faults in cable-stayed towers, which are part of the infrastructure of Brazil’s interconnected electrical system. The proposed method increases system reliability and minimizes the risk of service failure and tower collapse through the introduction of predictive maintenance methods based on artificial intelligence, which will ultimately benefit the end consumer. The proposed signal processing and interpretation methods are based on a machine learning approach, where the tower vibration is acquired from accelerometers that measure the dynamic response caused by the effects of the environment on the towers through wind and weather conditions. Data-based models were developed to obtain a representation of health degradation, which is primarily based on the finite element model of the tower, subjected to wind excitation. This representation is also based on measurements using a mockup tower with different types of provoked degradation that was subjected to ambient changes in the laboratory. The sensor signals are preprocessed and submitted to an autoencoder neural network to minimize the dimensionality of the resources involved, being analyzed by a classifier, based on a Softmax configuration. The results of the proposed configuration indicate the possibility of early failure detection and evolution evaluation, providing an effective failure detection and monitoring system.

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Failure Detection in Induction Motors Using Non-supervised Machine Learning Algorithms

Systems and Information Sciences - Advances in Intelligent Systems and Computing ◽

10.1007/978-3-030-59194-6_5 ◽

2020 ◽

pp. 48-59

Author(s):

David S. Toscano ◽

Enrique V. Carrera

Keyword(s):

Machine Learning ◽

Induction Motors ◽

Learning Algorithms ◽

Failure Detection ◽

Machine Learning Algorithms ◽

Supervised Machine Learning

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Chronic Heart Failure Detection from Heart Sounds Using a Stack of Machine-Learning Classifiers

2017 International Conference on Intelligent Environments (IE) ◽

10.1109/ie.2017.19 ◽

2017 ◽

Cited By ~ 4

Author(s):

Martin Gjoreski ◽

Monika Simjanoska ◽

Anton Gradisek ◽

Ana Peterlin ◽

Matjaz Gams ◽

...

Keyword(s):

Machine Learning ◽

Heart Failure ◽

Chronic Heart Failure ◽

Failure Detection ◽

Heart Sounds ◽

Machine Learning Classifiers ◽

Learning Classifiers

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Heart Failure Detection Using Quantum-Enhanced Machine Learning and Traditional Machine Learning Techniques for Internet of Artificially Intelligent Medical Things

Wireless Communications and Mobile Computing ◽

10.1155/2021/1616725 ◽

2021 ◽

Vol 2021 ◽

pp. 1-16

Author(s):

Yogesh Kumar ◽

Apeksha Koul ◽

Pushpendra Singh Sisodia ◽

Jana Shafi ◽

Verma Kavita ◽

...

Keyword(s):

Machine Learning ◽

Heart Failure ◽

Random Forest ◽

Learning Algorithms ◽

Failure Detection ◽

Random Forest Classifier ◽

Machine Learning Algorithms ◽

Machine Learning Techniques ◽

Research Progress ◽

Record Management

Quantum-enhanced machine learning plays a vital role in healthcare because of its robust application concerning current research scenarios, the growth of novel medical trials, patient information and record management, procurement of chronic disease detection, and many more. Due to this reason, the healthcare industry is applying quantum computing to sustain patient-oriented attention to healthcare patrons. The present work summarized the recent research progress in quantum-enhanced machine learning and its significance in heart failure detection on a dataset of 14 attributes. In this paper, the number of qubits in terms of the features of heart failure data is normalized by using min-max, PCA, and standard scalar, and further, has been optimized using the pipelining technique. The current work verifies that quantum-enhanced machine learning algorithms such as quantum random forest (QRF), quantum K nearest neighbour (QKNN), quantum decision tree (QDT), and quantum Gaussian Naïve Bayes (QGNB) are better than traditional machine learning algorithms in heart failure detection. The best accuracy rate is (0.89), which the quantum random forest classifier attained. In addition to this, the quantum random forest classifier also incurred the best results in F 1 score, recall and, precision by (0.88), (0.93), and (0.89), respectively. The computation time taken by traditional and quantum-enhanced machine learning algorithms has also been compared where the quantum random forest has the least execution time by 150 microseconds. Hence, the work provides a way to quantify the differences between standard and quantum-enhanced machine learning algorithms to select the optimal method for detecting heart failure.

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Azure machine learning studio and SCADA data for failure detection and prediction purposes: A case of wind turbine generator

IOP Conference Series Materials Science and Engineering ◽

10.1088/1757-899x/1201/1/012086 ◽

2021 ◽

Vol 1201 (1) ◽

pp. 012086

Author(s):

A El-Menshawy ◽

Z Gul ◽

I El-Thalji

Keyword(s):

Machine Learning ◽

Wind Turbine ◽

Failure Detection ◽

Health Condition ◽

Machine Learning Algorithms ◽

Training Data ◽

Turbine Generator ◽

Wind Turbine Generator ◽

Boosted Decision Tree ◽

Potential Benefits

Abstract Most industrial systems have supervisory control and data acquisition (SCADA) systems that collect and store process parameters. SCADA data is seen as a valuable source to get and extract insights about the asset health condition and associated maintenance operations. It is still unclear how appliable and valid insights SCADA data might provide. The purpose of this paper is to explore the potential benefits of SCADA data for maintenance purposes and discuss the limitations from a machine learning perspective. In this paper, a two-year SCADA data related to a wind turbine generator is extracted and analysed using several machine learning algorithms, i.e., two-class boosted decision tree, two-class decision forest, k-means clustering on Azure ML learning studio. It is concluded that the SCADA data can be useful for failure detection and prediction once rich training data is given. In a failure prediction context, data richness means ensuring that fault features are presented in the training data. Moreover, the logs file can be used as labelled data to supervise some algorithms once they are reported in a more rigorous manner (timing, description).

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Integrated Relaxation Pressure Classification and Probe Positioning Failure Detection in High-Resolution Esophageal Manometry Using Machine Learning

Sensors ◽

10.3390/s22010253 ◽

2021 ◽

Vol 22 (1) ◽

pp. 253

Author(s):

Zoltan Czako ◽

Teodora Surdea-Blaga ◽

Gheorghe Sebestyen ◽

Anca Hangan ◽

Dan Lucian Dumitrascu ◽

...

Keyword(s):

Machine Learning ◽

Deep Learning ◽

High Resolution ◽

Esophageal Manometry ◽

Region Of Interest ◽

Failure Detection ◽

Final Diagnosis ◽

Exact Moment ◽

Integrated Relaxation Pressure ◽

Deep Learning Model

High-resolution esophageal manometry is used for the study of esophageal motility disorders, with the help of catheters with up to 36 sensors. Color pressure topography plots are generated and analyzed and using the Chicago algorithm a final diagnosis is established. One of the main parameters in this algorithm is integrated relaxation pressure (IRP). The procedure is time consuming. Our aim was to firstly develop a machine learning based solution to detect probe positioning failure and to create a classifier to automatically determine whether the IRP is in the normal range or higher than the cut-off, based solely on the raw images. The first step was the preprocessing of the images, by finding the region of interest—the exact moment of swallowing. Afterwards, the images were resized and rescaled, so they could be used as input for deep learning models. We used the InceptionV3 deep learning model to classify the images as correct or failure in catheter positioning and to determine the exact class of the IRP. The accuracy of the trained convolutional neural networks was above 90% for both problems. This work is just the first step in fully automating the Chicago Classification, reducing human intervention.

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Heart Disease Prediction Based on Optimized Random Forest Model Using Machine Learning

International Journal of Scientific Research in Science and Technology ◽

10.32628/ijsrst218376 ◽

2021 ◽

pp. 337-340

Author(s):

Prof. R. A. Jamadar ◽

Aarati Garje ◽

Tejasvi Bhorde ◽

Vaishnavi Jadhav

Keyword(s):

Machine Learning ◽

Heart Disease ◽

Random Forest ◽

Failure Detection ◽

High Accuracy ◽

Random Forest Model ◽

Learning System ◽

Disease Prediction ◽

Forest Model ◽

Coronary Failure

Heart disease is one amongst the key causes of death now-a-days. Prediction of the center sickness is troublesome, time overwhelming and expensive, therefore we tend to try to beat it. This analysis is to assist individuals, as we all know prediction of upset may be a vital challenge and it’s expensive that most of the individuals can’t afford and lacking behind due to these, therefore to assist them for obtaining done this tests in low value, we tend to try to develop cardiovascular disease prediction system victimization machine learning. As there square measure several systems designed for machine-controlled coronary failure testing however it's some drawbacks like over fitting that we tend to try to beat in our system and implementing system which is able to show smart performance and have high accuracy as compared to alternative systems. Experiment is performed victimization on-line clinical coronary failure dataset. The projected methodology is a smaller amount complicated with high accuracy of report. They contributes towards study square measure as follows: one. AN intelligent learning system RSA-RF is projected for the machine-controlled detection of coronary failure. The projected RSA-RF model was projected and developed for the primary time for the center failure detection. Previously, RSA algorithms have shown winning applications in looking best hyper parameters of a model. This paper presents its application in looking best set of options. 2. The developed learning system improves coronary failure prediction of typical random forest model by three.3% and shows higher performance than eleven recently projected strategies and alternative state of the art machine learning models for coronary failure detection. Moreover, the projected methodology shows lower time complexness because it reduces the amount of options[1].

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