Study of Induction Motor Fault Detection and Diagnosis: Using Neural Network Techniques and Statistical Classifiers - Image 1

£75.50

About as cheap as it gets — within 5% of the all-time low it hit 1 month ago.

£76 today · all-time low £74 (Aug 2026) · usually the usual

LAP Lambert Academic Publishing Study of Induction Motor Fault Detection and Diagnosis: Using Neural Network Techniques and Statistical Classifiers

Name: Study of Induction Motor Fault Detection and Diagnosis: Using Neural Network Techniques and Statistical Classifiers
Brand: LAP Lambert Academic Publishing
Price: 75.5 GBP
Availability: InStock

Electromagnetic Theory

Price data last checked 31 day(s) ago - refreshing...

View at Amazon

We'll watch every seller, every day. One email when your price arrives.

Grey patches = out of stock. Cheaper = lower on the chart. Hover for exact prices.

Last 60 days · 60 data points (no recent data)

Historical

Generating forecast…

View at Amazon ↗

Electromagnetic Theory

Price data last checked 31 day(s) ago - refreshing...

NEW HERE?

Amazon shows you one price. We show you all of them.

Tosheroon watches Amazon prices so you don't have to. Every product on Amazon has a price history — we make it visible. Set the price you'd actually pay, and we'll email you the second it gets there. No app, no account, one email.

WHAT'S ON THIS PAGE

↑ Price chart
when this has been cheap or pricey

↑ Forecast
where the price is heading next

↓ Statistics
all-time high & low, recent range

↑ Price alert
name your number, we'll email you

Price Distribution

Price distribution over 60 days • 3 price levels

Days at Price

Current Price

Price Analysis

Most common price: £75 (30 days, 50.0%)

Price range: £74 - £76

Price levels: 3 different prices over 60 days

Description

In this book, the authors evaluated the performance of the developed ANN based classifiers for detection of four fault conditions, Healthy (H), Interturn short circuit (I), Eccentricity (E). and Both i.e. Interturn and Eccentricity combine (B) of three phase induction motor and examined the results. Instead of current or vibration spectra simple statistical parameters containing fault information is used, which gives the satisfactory results. MLP NN, RBF NN, RBF-MLP Cascade, SOM NN, GFFD NN, and SVM are optimally designed and after completion of the training, the learned network is tested to detect different types of faults. Sensitivity Analysis and Principal Component Analysis (PCA), these two techniques for dimensionality reduction is applied for each network and reduction in complexity is compared. For the comparison two statistical methods CART and D.A. are verified but results are inferior as compared to NN based schemes. Robustness of proposed classifier to the noise is good which is checked by introducing controlled noise, and for training, cross validation and testing experimental real data is used, so this scheme can be suggested for the real world industrial application.

Product Specifications

Brand: LAP Lambert Academic Publishing
Format: paperback
ASIN: 6202803649
Category: Books > Subjects > Science, Nature & Maths > Engineering & Technology > Electrical > Electromagnetic Theory
Domain: Amazon UK
Release Date: 07 October 2020
Listed Since: 01 November 2020

Barcode

No barcode data available

About as cheap as it gets — within 5% of the all-time low it hit 1 month ago.

LAP Lambert Academic Publishing Study of Induction Motor Fault Detection and Diagnosis: Using Neural Network Techniques and Statistical Classifiers

Price Distribution

Price Analysis

Description

Product Specifications

Barcode

Similar Products You Might Like

Monitoring of Operating Induction Motors: An Experimental Design for Detection and Diagnosis of Incipient Faults Using A Simplified Novel Technique

Modelling and Analysis of Induction Motor Drives: Analysis to enhance motor performance using controllers

Fault diagnosis of induction motor fed by frequency converter. The signal signature analysis technique

NEUTRALPOINT CLAMPED MULTI LEVEL INVERTER FOR INDUCTION MOTOR CONTROL: STUDY AND INVESTIGATION ON DIRECT TORQUE CONTROL

Fault-Tolerant in Voltage Source Inverter for Induction Motor Drive: Pattern Recognition, Fault Diagnosis, and Reconfiguration in Inverters Using AI

Research on fault diagnosis of permanent magnet synchronous motor: based on small sample learning

Multiple 3-phase Fault Tolerant Permanent Magnet Machine Drives: Design and Control

Introduction To Electrical Machines: Electrical Machines

Detection & Isolation of Faults in Electrical System with Optimization

Electric Machinery and Drives: An Electromagnetics Perspective (IEEE Press Series on Power and Energy Systems)

A Smart Control System of Electric Motor by Computer Interfacing: Real time smart control & advance monitoring system

Special Electrical Machines: A Comprehensive Knowledge

PARAMETER IDENTIFICATION FOR INDUCTION MOTOR DRIVES: USING ARTIFICIAL NEURAL NETWORKS AND FUZZY LOGIC

Induction Motors: Applications, Control and Fault Diagnostics

Neural Adaptive Controller for Power System Stability Improvement

Fault Diagnosis of Permanent Magnets Machines

Scalable Rule Induction Systems: Design and Analysis

Next Generation Hi-Tech Digital e-Society: Beyond Industry 4.0 Automatons

New Neural Network for Real-Time Human Dynamic Motion Prediction

Industry 4.0: The role of industry 4.0 in agri-food, process safety & environmental protection

Design Optimization of Multilevel Inverters for Voltage Regulation

Modeling & Fault Diagnosis of IM by using Intelligent Control: University Of Malaya/Malaysia

Mechatronic Systems: Foundations and Applications

Enhancement of Power System Quality using Advanced Control Techniques

About as cheap as it gets — within 5% of the all-time low it hit 1 month ago.

LAP Lambert Academic Publishing Study of Induction Motor Fault Detection and Diagnosis: Using Neural Network Techniques and Statistical Classifiers

Price History & Forecast

Price Distribution

Price Analysis

Description

Product Specifications

Barcode

Similar Products You Might Like

Monitoring of Operating Induction Motors: An Experimental Design for Detection and Diagnosis of Incipient Faults Using A Simplified Novel Technique

Modelling and Analysis of Induction Motor Drives: Analysis to enhance motor performance using controllers

Fault diagnosis of induction motor fed by frequency converter. The signal signature analysis technique

NEUTRALPOINT CLAMPED MULTI LEVEL INVERTER FOR INDUCTION MOTOR CONTROL: STUDY AND INVESTIGATION ON DIRECT TORQUE CONTROL

Fault-Tolerant in Voltage Source Inverter for Induction Motor Drive: Pattern Recognition, Fault Diagnosis, and Reconfiguration in Inverters Using AI

Research on fault diagnosis of permanent magnet synchronous motor: based on small sample learning

Multiple 3-phase Fault Tolerant Permanent Magnet Machine Drives: Design and Control

Introduction To Electrical Machines: Electrical Machines

Detection & Isolation of Faults in Electrical System with Optimization

Electric Machinery and Drives: An Electromagnetics Perspective (IEEE Press Series on Power and Energy Systems)

A Smart Control System of Electric Motor by Computer Interfacing: Real time smart control & advance monitoring system

Special Electrical Machines: A Comprehensive Knowledge

PARAMETER IDENTIFICATION FOR INDUCTION MOTOR DRIVES: USING ARTIFICIAL NEURAL NETWORKS AND FUZZY LOGIC

Induction Motors: Applications, Control and Fault Diagnostics

Neural Adaptive Controller for Power System Stability Improvement

Fault Diagnosis of Permanent Magnets Machines

Scalable Rule Induction Systems: Design and Analysis

Next Generation Hi-Tech Digital e-Society: Beyond Industry 4.0 Automatons

New Neural Network for Real-Time Human Dynamic Motion Prediction

Industry 4.0: The role of industry 4.0 in agri-food, process safety & environmental protection

Design Optimization of Multilevel Inverters for Voltage Regulation

Modeling & Fault Diagnosis of IM by using Intelligent Control: University Of Malaya/Malaysia

Mechatronic Systems: Foundations and Applications

Enhancement of Power System Quality using Advanced Control Techniques