PDF | On Jan 1, 2021, Vijayalakshmi S and others published Condition Monitoring of Industrial Motors using Machine Learning Classifiers | Find, read and cite all the research you need on ResearchGate
Image classification using convolutional neural networks (CNNs) is critical for broader industrial applications like defect detection. To protect sensitive data during the industrial process, increasing institutions are highly interested in training CNN classifiers collaboratively with federated learning (FL). However, the existing FL solutions cannot …
In the recent development of induction motors fault diagnosis, machine-learning algorithms have been implemented to replace the need for experts in fault diagnostic decisions. In industrial practice, faults exhibit symptoms but not in the early stage. This condition limits the availability of fault datasets for machine-learning classifier training. Therefore, the …
This paper describes the development of an Industrial Learning Classifier System for application in the steel industry. The real domain problem was the prediction and diagnosis of product quality ...
This article presents the first results from using a learning classifier system capable of performing adaptive computation with deep neural networks. Individual classifiers within the population are composed of two neural networks. The first acts as a gating or guarding component, which enables the conditional computation of an …
This paper describes the development of an Industrial Learning Classifier System for applicatiQn in the steel industry. The real domain problem was the prediction and …
Forecasting faults of industrial equipment using machine learning classifiers Abstract: This work presents a predictive maintenance methodology so as to forecast possible equipment stoppages (or faults) of an industrial equipment for anode production along with the fault type in real time, utilizing process sensor data from operation periods.
The Industrial Internet of Things (IIoT), which integrates sensors into the manufacturing system, provides new paradigms and technologies to industry. ... Stacking is an ensemble learning technique that combines predictions of different classifiers using a learning algorithm. In stacking, first, various individual classifiers are trained in ...
To solve this problem, this study used a tri-training architecture-based semi-supervised ensemble learning method for industrial fault diagnosis under a small training set. Specifically, a heterogeneous classifier was utilised to increase the diversity of the base classifiers, and noise samples were removed through a sample pruning operation.
Keywords— Data-driven approach, Industry 4.0, Machine learning, Predictive Maintenance, RUL prognosis, Smart sensors. ... etc. Unsupervised machine learning classifiers were used by Kolokas et ...
Machine learning with considering data privacy-preservation and personalized models has received attentions, especially in the manufacturing field. The data often exist in the form of isolated islands and cannot be shared because of data privacy in real industrial scenarios. It is difficult to gather the data to train a personalized model ...
Explore powerful machine learning classification algorithms to classify data accurately. Learn about decision trees, logistic regression, support vector machines, and more. Master the art of predictive modelling and enhance your data analysis skills with these essential tools.
Thus, the agricultural industry continues to look for practical approaches to maximize food production in light of the recurring climate change and rising population [108]. ... Deep Learning classifiers exhibit high performance in terms of accuracy and efficiency in a large number of datasets [[101], ...
Deep Neural Networks (DNNs) are vulnerable to deliberately crafted adversarial examples. In the past few years, many efforts have been spent on exploring query-optimisation attacks to find adversarial examples of either black-box or white-box DNN models, as well as the defending countermeasures against those attacks. In this …
Automatic defect inspection is an important application for the development of smart factories in the era of Industry 4.0. It gathers data from production lines to train a model to automatically recognize certain types of defects. However, the defect types may vary in the production process, and it is difficult for the old model to adapt to new types …
The need for networking in smart industries known as Industry 5.0 has grown critical, and it is especially important for the security and privacy of the applications. To counter threats to important consumers devices' sensitive data, various applications of smart industries require intelligent schemes and architectures. The data which is …
3. Result and discussion. Fig. 3 shows the comparison of the average accuracy performance of four different machine learning classifier models in terms of wafer defect classification. Out of the four machine learning classifiers evaluated, Logistic Regression classifier gives the best classification accuracy with 86.0% during training …
Data-based equipment fault detection and diagnosis is an important research area in the smart factory era, which began with the Fourth Industrial Revolution. Steel manufacturing is a typical processing industry, and efficient equipment operation can improve product quality and cost. Steel production systems require precise control of the …
Founded upon the premises of big data and deep learning, machine learning enables us to go beyond explicitly programing computers to perform certain actions. It empowers us to teach them how to ...
We use three machine learning classifiers, namely K-Nearest Neighbors (k-NN), Support Vector Machine (SVM), and the Random Forest Classifier (RFC). This study aims to compare and determine the best classification algorithm to be used for crab's molting identification. The experimental results show that, KNN is the best classification ...
Features of Random Forest . Ensemble Method: Random Forest uses the ensemble learning technique, where multiple learners (decision trees, in this case) are trained to solve the same problem and combined to get better results.The ensemble approach improves the model's accuracy and robustness. Handling Both Types of Data: …
industrial environment was examined to form the basis of an industrialised LCS technique. This unique starting point lead to insight into the operation of the LCS …
A novel traffic classifier called flow transformer is proposed to perform traffic analysis with flow sequences, which leverages multihead attention mechanism to strengthen the information interaction between related flows and outperforms state-of-the-art methods with a large margin. With the development of the Industrial Internet of …
In this article, we present a federated lightweight relation network (FLRN), a lightweight industrial image classifier based on our federated few-shot learning (FFSL) …
An efficient text classifier can automatically distinguish the data into categories efficiently with the use NLP algorithms. Text Classification is an example of supervised machine learning task since a labelled dataset containing text documents and their labels is used for train a classifier. ... Industrial Tools and Hardware products …
Recently, various sophisticated methods, including machine learning and artificial intelligence, have been employed to examine health-related data. Medical professionals are acquiring enhanced diagnostic and treatment abilities by utilizing machine learning applications in the healthcare domain. Medical data have been used by many …
In this paper, a multiple classifier machine learning (ML) methodology for predictive maintenance (PdM) is presented. PdM is a prominent strategy for dealing with maintenance issues given the increasing need to minimize downtime and associated costs. One of the challenges with PdM is generating the so-called "health factors," or …
In this study, a SVM classifier with a supervised machine learning algorithm was developed to predict hearing impairment caused by a variety of industrial noise exposures. The ability to generate rules from data automatically and predict unknown data make machine learning a promising tool to predict hearing trauma from any industrial …
Learning Classifier Systems represent a potentially useful tool that combines the transparency of symbolic approaches (such as Decision Trees) with the learning ability …
2.1. The anomaly detector structure. We use the proposed method of transforming a class in the input space X to a compact region in the feature space Y, to build a hybrid one-class classifier to detect faults in industrial equipment.It consists of two stages. The first one is a CNN, i.e., a deep learning-based feature extractor, which …