Internet of Things Enabled Device Fault Prediction System Using Machine Learning

Title

Internet of Things Enabled Device Fault Prediction System Using Machine Learning

Creator

Bhavana K.; Nekkanti V.; Jayapandian N.

Description

Internet of Things (IOT) started as a niche market for hobbyists and has evolved into a huge industry. This IoT is convergence of manifold technologies, real-time analytics, machine learning and Artificial Intelligence. It has given birth to many consumer needs like home automation, prior device fault detection, health appliances and remote monitoring applications. Programmed recognition and determination of different kinds of machine disappointment is a fascinating process in modern applications. Different sorts of sensors are utilized to screen flaws that is discovers vibration sensors, sound sensors, warm sensors, infrared cameras, light cameras, and other multispectral sensors. The modern devices are becoming ubiquitous and pervasive in day to day life. This device is need for reliable and predicate algorithms. This article is primarily emphases on the prediction of faults in real life appliances making our day to day life easier. Here, the database of the device includes previous faults which are restored in online by using cloud computing technology. This will help in the prediction of the faults in the devices that are to be ameliorated. It additionally utilizes Nae Bayes calculation for shortcoming location in the gadgets. The proposed model of this article is involves the monitoring of each and every home appliance through internet and thereby detect faults without much of human intervention. Springer Nature Switzerland AG 2020.

Source

Lecture Notes in Networks and Systems, Vol-98, pp. 920-927.

Date

2020-01-01

Publisher

Springer

Subject

Cloud computing; Home appliance; Internet of Things; Machine learning; Naive Bayes; Sensors

Coverage

Bhavana K., Department of Computer Science and Engineering, CHRIST (Deemed to Be University), Kengeri Campus, Bangalore, India; Nekkanti V., Department of Computer Science and Engineering, CHRIST (Deemed to Be University), Kengeri Campus, Bangalore, India; Jayapandian N., Department of Computer Science and Engineering, CHRIST (Deemed to Be University), Kengeri Campus, Bangalore, India

Rights

Restricted Access

Relation

ISSN: 23673370

Format

Online

Language

English

Type

Book chapter

Identifier

https://doi.org/10.1007/978-3-030-33846-6_101

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85075066770&doi=10.1007%2f978-3-030-33846-6_101&partnerID=40&md5=22d0fb24eaac25d52c42f2ca8d9ff962