IoT-Based Vibration Analytics of Electrical Machines

Ganga, D.; Ramachandran, V.

doi:10.1109/jiot.2018.2835724

Cited by 43 publications

(23 citation statements)

References 5 publications

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“…A comprehensive investigation has been made [14] that shifts the focus from the monitoring of specific machine components for fault prognosis to an approach scanning the overall system execution in an integrated manner to deliver desired performance for the application in an optimum manner. The authors address the challenge in the determination of absolute vibration thresholds adaptive to the machine operating conditions for reliable condition monitoring.…”

Section: State Of the Artmentioning

confidence: 99%

An IoT Based Cloud Deployment Framework for Effective Classification of Machine Conditions

Dhandapani¹,

Ramachandran²

2021

Internet of Things

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Cloud services are proposed for real-time data acquisition, data classification, data processing and decision making, which are highly interconnected services for effective condition monitoring of electrical machines. The proposed Software as a Service, Storage as a Service and Platform as a Service layers address the challenges of data storage and scalability while making analysis on the cluster of machines in an Industrial Environment. An experimental setup consisting of two DC motors coupled to AC Generator operating at different locations is considered to evolve the proposed model for effective integrated monitoring and decision making. This cloud-based vibration monitoring model provides services for data acquisition from the IoT devices mounted on the shafts of the DC motors, data storage to store the enormous amount of acquired signal data from multiple sensors, data classification of vibration signals for effective statistical analysis to estimate adaptive cluster of thresholds and appropriate decision-making services on demand over the Internet to utilize the reliable service of the machines in a persistent way. The computational engine will do inherent statistical analysis of the vibration signals to estimate the cluster of thresholds adaptive to various operating conditions. The services have been deployed without any limitation in a cloud environment and the industrial applications can share information using the deployed services from anywhere on demand basis. The deployed cloud service for the enhanced statistical classification algorithm eliminates the false identification of failures, which not only increase the availability of machines for intended operations but also reduce the maintenance cost. The resulting threshold values are compared with that of the vibration analysis carried out on the machine beds locally using myRIO for data acquisition in LabVIEW and the proposed model ensures the integrity in appropriate decision making with assured scalability.

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Section: State Of the Artmentioning

confidence: 99%

An IoT Based Cloud Deployment Framework for Effective Classification of Machine Conditions

Dhandapani¹,

Ramachandran²

2021

Internet of Things

Self Cite

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“…The objective of classification is to develop a classifier that learns the distribution of patterns in the set of labeled data. Classification has been used in numerous IoT use-cases including real-time ECG monitoring [40], twitter sentiment analysis [41], ebola virus outbreak control [42], real-time monitoring of breast cancer patients [43], automatic people counter in stores [44], real-time fall detection system for elderly people [45], defect detection in machines [46], cardiac arrest prediction [47], video surveillance [48], rice disease monitor and control [49], real-time condition monitoring of electric machines [50], etc.…”

Section: Data Mining Techniquesmentioning

confidence: 99%

Data Analytics in the Internet of Things: A Survey

Saleem

Chishti

2019

SCPE

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The plethora of sensors deployed in Internet of Things (IoT) environments generate unprecedented volumes of data, thereby creating a data deluge. Data collected from these sensors can be used to comprehend, examine and control intricate environments around us, facilitating greater intelligence, smarter decision-making, and better performance. The key challenge here is how to mine out proﬁcient information from such immense data. Copious solutions have been put forth to obtain valuable inferences and insights, however, these solutions are still in their developing stages. Moreover, conventional procedures do not address the surging analytical demands of IoT systems. Motivated to resolve this concern, this work investigates the key enablers for performing desired data analytics in IoT applications. A comprehensive survey on the identiﬁed key enablers including their role in IoT data analytics, use cases in which they have been applied and the corresponding IoT applications for the use cases is presented. Furthermore, open research challenges and future research opportunities are also discussed. This article can be used as a basis to foster advanced research in the arena of IoT data analytics.

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“…In order to cope up with the Industry 4.0 many industries are adopting newer cutting edge technologies such as Machine learning, Deep neural networks, Block chain and Internet of Things. The IoT has found its applications in all walks of life from agriculture [1], Energy [2]- [3], Industrial Automation [4], Medical [5]- [6], Transportation, Smart city, logistics and in all possible domains [7]- [9].…”

Section: A Generalmentioning

confidence: 99%

Wireless Monitoring Of Industrial Drives Using Internet Of Things Technology (Iot)

Kaliappan,

Ponkarthika,

Gowrishankar

et al. 2019

IJEAT

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Electrical motors and drives consumes about 45% of the power generation. However, if the electrical machines are not maintained properly the drives consumes about 5% to 10 % of excess power which affects the productivity and revenue. Continuous monitoring of critical parameters of the drives is crucial for an industry which continuously operates large number of electrical drives. Before the advent of Internet of Things (IoT) technology, monitoring of the machine parameters were carried out using Supervisory Control and Data Acquisition (SCADA), which can store data temporarily, after which that data will be overwritten by more recent data. Also the online monitoring of the machine is not feasible. In the IoT technology the data collected can be safely stored in the Cloud and retrieved whenever needed. Moreover the data collected can be easy integrated through any platform to any application, which benefits the end user by cutting down huge investment costs. In the presented work, the machine parameters like current, voltage, speed and number of working hours of the machine, temperature are measured using the sensors for continuous monitoring. The data is collected and processed using microcontroller and transferred to a remote server wirelessly using Message Queuing Telemetry Transport (MQTT) protocol. The received data is then sent to the telegram server. Any deviation from the safe operating conditions is reported to the server and control room through chatbot in telegram mobile app for immediate action

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IoT-Based Vibration Analytics of Electrical Machines

Cited by 43 publications

References 5 publications

An IoT Based Cloud Deployment Framework for Effective Classification of Machine Conditions

An IoT Based Cloud Deployment Framework for Effective Classification of Machine Conditions

Data Analytics in the Internet of Things: A Survey

Wireless Monitoring Of Industrial Drives Using Internet Of Things Technology (Iot)

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