Estimating Remaining Useful Life in Machines Using Artificial Intelligence: A Scoping Review

The remaining useful life (RUL) estimations become one of the most essential aspects of predictive maintenance (PdM) in the era of industry 4.0. Predictive maintenance aims to minimize the downtime of machines or process, decreases maintenance costs, and increases the productivity of industries. The primary objective of this bibliometric paper is to understand the scope of literature available related to RUL prediction. Scopus database is used to perform the analysis of 1673 extracted scientific literature from the year 1985 to 2020. Based on available published documents, analysis is done on the year-wise publication data, document types, language-wise distribution of documents, funding sponsors, authors contributions, affiliations, document wise citations, etc. to give an in-depth view of the research trends in the area of RUL prediction. The paper also focuses on the available maintenance methods, predictive maintenance models, RUL models, deep learning algorithms for RUL prediction challenges and future directions in the RUL prediction area

Fractional abundances study of macronutrients in soil using hyperspectral remote sensing

In agriculture, soil fertility is maintained by using the compost, which contains Nitrogen (N), Phosphorus (P) and Potassium (K). Thus, it is required to acquire information about fertility status of soil and to apply the essential amount of composts. The laboratory-based chemical analysis methods for soil macronutrients test can be laborious, time-consuming, cost-intensive and destructive in nature. To overcome these issues, the hyperspectral remote sensing is employed for identification and determination of macronutrients of soil. The objective of this study is spectral unmixing of compositions of soil and NPK compost by using Derivative Analysis for Spectral Unmixing (DASU) approach. The proposed methodology has been tested for soil samples collected from an area located around Roorkee, UK, India. The applied methodology studies the spectral reflectance by using spectroradiometer data. The spectral regions 989.3 nm for pure NPK compost and 2195.1 nm for pure soils have been found optimal spectral absorption features. Accuracy assessment has been carried out on the basis of linear regression model between the true and estimated abundances. The coefficient of determination (R2) values for compositions of silt clay soil and NPK compost has been found at 989.3 nm spectral region as 0.892, 0.897 for compositions of loamy soil and NPK compost and 0.906 for sandy soil and NPK compost. Similarly, R2 values obtained at 2195.1 nm spectral region for silt clay soil and NPK compost is 0.932, 0.926 for compositions of loamy soil and NPK compost and 0.933 for sandy soil and NPK compost. The output of this study provides the fractional abundances of compositions of soil and NPK compost. Further, the results have been validated in laboratory by using chemical analysis methods. Thus, it may be concluded that hyperspectral remote sensing may be used in situ to estimate soil fertility status of farm soil

Remaining Useful-Life Prediction of the Milling Cutting Tool Using Time–Frequency-Based Features and Deep Learning Models

The milling machine serves an important role in manufacturing because of its versatility in machining. The cutting tool is a critical component of machining because it is responsible for machining accuracy and surface finishing, impacting industrial productivity. Monitoring the cutting tool’s life is essential to avoid machining downtime caused due to tool wear. To prevent the unplanned downtime of the machine and to utilize the maximum life of the cutting tool, the accurate prediction of the remaining useful life (RUL) cutting tool is essential. Different artificial intelligence (AI) techniques estimate the RUL of cutting tools in milling operations with improved prediction accuracy. The IEEE NUAA Ideahouse dataset has been used in this paper for the RUL estimation of the milling cutter. The accuracy of the prediction is based on the quality of feature engineering performed on the unprocessed data. Feature extraction is a crucial phase in RUL prediction. In this work, the authors considers the time–frequency domain (TFD) features such as short-time Fourier-transform (STFT) and different wavelet transforms (WT) along with deep learning (DL) models such as long short-term memory (LSTM), different variants of LSTN, convolutional neural network (CNN), and hybrid models that are a combination of CCN with LSTM variants for RUL estimation. The TFD feature extraction with LSTM variants and hybrid models performs well for the milling cutting tool RUL estimation

Fault Detection in Induction Motor Using Time Domain and Spectral Imaging-Based Transfer Learning Approach on Vibration Data

The induction motor plays a vital role in industrial drive systems due to its robustness and easy maintenance but at the same time, it suffers electrical faults, mainly rotor faults such as broken rotor bars. Early shortcoming identification is needed to lessen support expenses and hinder high costs by using failure detection frameworks that give features extraction and pattern grouping of the issue to distinguish the failure in an induction motor using classification models. In this paper, the open-source dataset of the rotor with the broken bars in a three-phase induction motor available on the IEEE data port is used for fault classification. The study aims at fault identification under various loading conditions on the rotor of an induction motor by performing time, frequency, and time-frequency domain feature extraction. The extracted features are provided to the models to classify between the healthy and faulty rotors. The extracted features from the time and frequency domain give an accuracy of up to 87.52% and 88.58%, respectively, using the Random-Forest (RF) model. Whereas, in time-frequency, the Short Time Fourier Transform (STFT) based spectrograms provide reasonably high accuracy, around 97.67%, using a Convolutional Neural Network (CNN) based fine-tuned transfer learning framework for diagnosing induction motor rotor bar severity under various loading conditions

Proceedings of National Conference on Relevance of Engineering and Science for Environment and Society

This conference proceedings contains articles on the various research ideas of the academic community and practitioners presented at the National Conference on Relevance of Engineering and Science for Environment and Society (R{ES}2 2021). R{ES}2 2021 was organized by Shri Pandurang Pratishthan’s, Karmayogi Engineering College, Shelve, Pandharpur, India on July 25th, 2021. Conference Title: National Conference on Relevance of Engineering and Science for Environment and SocietyConference Acronym: R{ES}2 2021Conference Date: 25 July 2021Conference Location: Online (Virtual Mode)Conference Organizers: Shri Pandurang Pratishthan’s, Karmayogi Engineering College, Shelve, Pandharpur, India