An Efficient IIoT-Based Smart Sensor Node for Predictive Maintenance of Induction Motors

Predictive maintenance is a vital aspect of the industrial sector,and the use of Industrial Internet of Things(IIoT)sensor nodes is becoming increasingly popular for detecting motor faults and monitoring motor conditions.An integrated approach for acquiring,processing,and wirelessly transmitting a large amount of data in predictive maintenance applications remains a significant challenge.This study presents an IIoT-based sensor node for industrial motors.The sensor node is designed to acquire vibration data on the radial and axial axes of the motor and utilizes a hybrid approach for efficient data processing via edge and cloud platforms.The initial step of signal processing is performed on the node at the edge,reducing the burden on a centralized cloud for processing data from multiple sensors.The proposed architecture utilizes the lightweight Message Queue Telemetry Transport(MQTT)communication protocol for seamless data transmission from the node to the local and main brokers.The broker’s bridging allows for data backup in case of connection loss.The proposed sensor node is rigorously tested on a motor testbed in a laboratory setup and an industrial setting in a rice industry for validation,ensuring its performance and accuracy in real-world industrial environments.The data analysis and results from both testbed and industrial motors were discussed using vibration analysis for identifying faults.The proposed sensor node is a significant step towards improving the efficiency and reliability of industrial motors through realtime monitoring and early fault detection,ultimately leading to minimized unscheduled downtime and cost savings.

Improving Routine Immunization Coverage Through Optimally Designed Predictive Models

Routine immunization(RI)of children is the most effective and timely public health intervention for decreasing child mortality rates around the globe.Pakistan being a low-and-middle-income-country(LMIC)has one of the highest child mortality rates in the world occurring mainly due to vaccine-preventable diseases(VPDs).For improving RI coverage,a critical need is to establish potential RI defaulters at an early stage,so that appropriate interventions can be targeted towards such populationwho are identified to be at risk of missing on their scheduled vaccine uptakes.In this paper,a machine learning(ML)based predictivemodel has been proposed to predict defaulting and non-defaulting children on upcoming immunization visits and examine the effect of its underlying contributing factors.The predictivemodel uses data obtained from Paigham-e-Sehat study having immunization records of 3,113 children.The design of predictive model is based on obtaining optimal results across accuracy,specificity,and sensitivity,to ensure model outcomes remain practically relevant to the problem addressed.Further optimization of predictive model is obtained through selection of significant features and removing data bias.Nine machine learning algorithms were applied for prediction of defaulting children for the next immunization visit.The results showed that the random forest model achieves the optimal accuracy of 81.9%with 83.6%sensitivity and 80.3%specificity.The main determinants of vaccination coverage were found to be vaccine coverage at birth,parental education,and socioeconomic conditions of the defaulting group.This information can assist relevant policy makers to take proactive and effective measures for developing evidence based targeted and timely interventions for defaulting children.

Threefold Optimized Forecasting of Electricity Consumption in Higher Education Institutions

Energy management benefits both consumers and utility companiesalike. Utility companies remain interested in identifying and reducing energywaste and theft, whereas consumers’ interest remain in lowering their energyexpenses. A large supply-demand gap of over 6 GW exists in Pakistan asreported in 2018. Reducing this gap from the supply side is an expensiveand complex task. However, efficient energy management and distributionon demand side has potential to reduce this gap economically. Electricityload forecasting models are increasingly used by energy managers in takingreal-time tactical decisions to ensure efficient use of resources. Advancementin Machine-learning (ML) technology has enabled accurate forecasting ofelectricity consumption. However, the impact of computation cost affordedby these ML models is often ignored in favour of accuracy. This studyconsiders both accuracy and computation cost as concurrently significantfactors because together they shape the technology environment as well ascreate economic impact. Thus, a three-fold optimized load forecasting modelis proposed which includes (1) application specific parameters selection, (2)impact of different dataset granularities and (3) implementation of specificdata preparation. It deploys and compares the widely used back-propagationArtificial Neural Network (ANN) and Random Forest (RF) models for theprediction of electricity consumption of buildings within a university. In addition to the temporal and historical power consumption date as input parameters, the study also embeds weather data as well as university operationalcalendars resulting in improved performance. The outcomes are indicativethat the granularity i.e. the scale of details in data, and set of reduced and fullinput parameters impact performance accuracies differently for ANN and RFmodels. Experimental results show that overall RF model performed betterboth in terms of accuracy as well as computational time for a 1-min, 15-minand 1-h dataset granularities with the mean absolute percentage error (MAPE)of 2.42, 3.70 and 4.62 in 11.1 s, 1.14 s and 0.3 s respectively, thus well suitedfor a real-time energy monitoring application.