scholarly journals Fault severity detection of a worm gearbox based on several feature extraction methods through a developed condition monitoring system

2021 ◽  
Vol 3 (1) ◽  
Author(s):  
Berkan Hızarcı ◽  
Rafet Can Ümütlü ◽  
Zeki Kıral ◽  
Hasan Öztürk
Keyword(s):  
Fault Detection ◽  
Condition Monitoring ◽  
Extraction Methods ◽  
Poincaré Plot ◽  
Vibration Data ◽  
Sliding Motion ◽  
Fault Severity ◽  
Fault Features ◽  
Gearbox Vibration ◽  
Condition Monitoring System

AbstractThis study presents the severity detection of pitting faults on worm gearbox through the assessment of fault features extracted from the gearbox vibration data. Fault severity assessment on worm gearbox is conducted by the developed condition monitoring instrument with observing not only traditional but also multidisciplinary features. It is well known that the sliding motion between the worm gear and wheel gear causes difficulties about fault detection on worm gearboxes. Therefore, continuous monitoring and observation of different types of fault features are very important, especially for worm gearboxes. Therefore, in this study, time-domain statistics, the features of evaluated vibration analysis method and Poincaré plot are examined for fault severity detection on worm gearbox. The most reliable features for fault detection on worm gearbox are determined via the parallel coordinate plot. The abnormality detection during worm gearbox operation with the developed system is performed successfully by means of a decision tree.

A Conceptual Framework for the Design and Development of Automated Online Condition Monitoring System for Elevators (AOCMSE) Using IoT

2019 ◽  
pp. 189-207
Author(s):  
M. S. Starvin ◽  
A. Sherly Alphonse
Keyword(s):  
Conceptual Framework ◽  
Monitoring System ◽  
Condition Monitoring ◽  
Principal Component ◽  
Design And Development ◽  
Vibration Data ◽  
Vibration Sensors ◽  
Traction Motors ◽  
Condition Monitoring System ◽  
Learning Machine

The reliability of an elevator system in a smart city is of great importance. This chapter develops a conceptual framework for the design and development of an automated online condition monitoring system for elevators (AOCMSE) using IoT techniques to avoid failures. The elevators are powered by the traction motors. Therefore, by placing vibration sensors at various locations within the traction motor, the vibration data can be acquired and converted to 2D grayscale images. Then, maximum response-based directional texture pattern (MRDTP) can be applied to those images which are an advanced method of feature extraction. The feature vectors can also be reduced in dimension using principal component analysis (PCA) and then given to extreme learning machine (ELM) for the classification of the faults to five categories. Thus, the failure of elevators and the consequences can be prevented by sending this detected fault information to the maintenance team.

Online Condition Monitoring of Traction Motor

2016 ◽  
pp. 489-523
Author(s):  
Anik Kumar Samanta ◽  
Arunava Naha ◽  
Devasish Basu ◽  
Aurobinda Routray ◽  
Alok Kanti Deb
Keyword(s):  
Fault Detection ◽  
Condition Monitoring ◽  
Traction Motor ◽  
Motor Current ◽  
Detection Technology ◽  
Current Voltage ◽  
Motor Current Signature Analysis ◽  
Complete Failure ◽  
Condition Monitoring System ◽  
The Cost

Squirrel Cage Induction Motors (SCIMs) are major workhorse of Indian Railways. Continuous online condition monitoring of the SCIMs like Traction Motor (TM) are essential to prevent unnecessary stoppage time in case of a complete failure. Before a complete failure, the TMs generally develop incipient or weak faults. Weak faults have minute influence on the motor performance but eventually leads to complete failure of the motor. If these weak faults are identified at the earliest then, a scheduled maintenance can be planned which will prevent any unplanned stoppage. The signals used for SCIM fault detection are motor current, voltage, vibration, temperature, voltage induced in search coil, etc. The most popular fault detection technology is based on Motor Current Signature Analysis (MCSA). MCSA based online and onboard TM condition monitoring system can be very useful for Indian railways to reduce the cost of operation and unplanned delay by shifting from unnecessary scheduled maintenance to condition-based maintenance of TM and other auxiliary SCIMs.

scholarly journals Train Type Identification at S&C

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Martina Kratochvílová ◽  
Jan Podroužek ◽  
Jiří Apeltauer ◽  
Ivan Vukušič ◽  
Otto Plášek
Keyword(s):  
Machine Learning ◽  
Monitoring System ◽  
Condition Monitoring ◽  
Dynamical Response ◽  
Vibration Data ◽  
Suitable Tool ◽  
Acceleration Data ◽  
Condition Monitoring System

The presented paper concerns the development of condition monitoring system for railroad switches and crossings that utilizes vibration data. Successful utilization of such system requires a robust and efficient train type identification. Given the complex and unique dynamical response of any vehicle track interaction, the machine learning was chosen as a suitable tool. For design and validation of the system, real on-site acceleration data were used. The resulting theoretical and practical challenges are discussed.

Fault detection of gearboxes using synchro-squeezing transform

2016 ◽  
Vol 23 (19) ◽  
pp. 3108-3127 ◽  
Author(s):  
B Hazra ◽  
A Sadhu ◽  
S Narasimhan
Keyword(s):  
Fault Detection ◽  
Mathematical Structure ◽  
Intrinsic Mode Functions ◽  
Robust Detection ◽  
Time Frequency ◽  
Vibration Data ◽  
Condition Indicators ◽  
Mode Decomposition ◽  
Gearbox Vibration ◽  
The Individual

This paper presents a novel fault detection method for gearbox vibration signatures using the synchro-squeezing transform (SST). Premised upon the concept of time-frequency (TF) reassignment, the SST provides a sharp representation of signals in the TF plane compared to many popular TF methods. Additionally, it can also extract the individual components, called intrinsic mode functions or IMFs, of a nonstationary multi-component signal, akin to empirical mode decomposition. The rich mathematical structure based on the continuous wavelet transform makes synchro-squeezing a promising candidate for gearbox diagnosis, as such signals are frequently constituted out of multiple amplitude and frequency modulated signals embedded in noise. This work utilizes the decomposing power of the SST to extract the IMFs from gearbox signals, followed by the application of both condition indicators and fault detection to gearbox vibration data. For robust detection of faults in gear-motors, a fault detection technique based on time-varying auto-regressive coefficients of IMFs as features is utilized. The sequential Karhunen–Loeve transform is employed on the condition indicators to select the appropriate window sizes on which the SST can be applied. This approach promises improved fault detection capability compared to applying condition indicators directly to the raw data. Laboratory experimental data obtained from a drivetrain diagnostics simulator and seeded fault tests from a helicopter gearbox provide test beds to demonstrate the robustness of the proposed algorithm.

A cloud-based condition monitoring system for fault detection in rotating machines using PROFINET process data

2021 ◽  
Vol 126 ◽  
pp. 103394
Author(s):  
Andre Luis Dias ◽  
Afonso Celso Turcato ◽  
Guilherme Serpa Sestito ◽  
Dennis Brandao ◽  
Rodrigo Nicoletti
Keyword(s):  
Fault Detection ◽  
Monitoring System ◽  
Condition Monitoring ◽  
Rotating Machines ◽  
Process Data ◽  
Condition Monitoring System

Application of Signal Processing for Motor Condition Monitoring Based on Filtered-Signals and Eliminated-Signals

2011 ◽  
Vol 378-379 ◽  
pp. 557-560
Author(s):  
Juggrapong Treetrong
Keyword(s):  
Signal Processing ◽  
Fault Detection ◽  
Condition Monitoring ◽  
Load Condition ◽  
Full Load ◽  
Fault Severity ◽  
Motor Condition ◽  
Measurement Noises ◽  
Stator Fault ◽  
New Procedures

This paper proposes new procedures of motor fault detection. The proposed methods are based on filtered-signals and eliminated-signals. Generally, the raw stator phase currents collected from the motors are firstly filtered in order to get rid of measurement noises. If the new signals are called “Filtered-Signals” and the signals eliminated from the raw stator phase currents are called “Eliminated-Signals”. The first proposed procedure is to detect the motor faults by spectrum of PSD slope from the filtered-signals. The second proposed procedure is to detect the motor faults by spectrum of the eliminated-signals. The both methods are tested on 3 different motor conditions: healthy, stator fault, and rotor fault motor at full load condition. The experiments show that the both methods can differentiate conditions clearly and they also can indicate the levels of fault severity. Thus, it can be effective when the both methods are applied simultaneously to analyze the faults

Wind Turbine Gearbox Planetary Stage Gear Damage Detection Using Vibration Data

2014 ◽  
Author(s):  
Jesse Hanna ◽  
Huageng Luo
Keyword(s):  
Wind Turbine ◽  
Condition Monitoring ◽  
Vibration Signal ◽  
Variable Speed ◽  
Wind Turbine Gearbox ◽  
Vibration Data ◽  
Planet Gear ◽  
Condition Monitoring System ◽  
The Many ◽  
Many Sources

Effective vibration based condition monitoring applied to the planetary stage of a wind turbine gearbox has been historically difficult. Numerous complications associated with the low speed and variable speed nature of a wind turbine gearbox as well as the many sources of vibration signal modulation and poor vibration transmission paths within the planetary stage itself have presented complex challenges around effectively monitoring the health of planetary stage components. The focus of this paper is the vibration behavior of planetary stage gear related damage and how this behavior can be accurately identified using vibration data. The theory behind this behavior and a case history showing the successful detection of planet gear damage and ring gear damage is presented. The damage detailed in this case is clearly identifiable in the data provided by the ADAPT.Wind condition monitoring system. Although this type of damage requires a gearbox replacement, prompt detection is important to avoid the risk of splitting the gearbox casing and damaging additional wind turbine components.

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