scholarly journals Vibration-Based Diagnostics of Radial Clearances and Bolts Loosening in the Bearing Supports of the Heavy-Duty Gearboxes

Sensors ◽  
2020 ◽  
Vol 20 (24) ◽  
pp. 7284
Author(s):  
Pavlo Krot ◽  
Volodymyr Korennoi ◽  
Radoslaw Zimroz
Keyword(s):  
Natural Frequency ◽  
Condition Monitoring ◽  
Diagnostic Methods ◽  
Bolted Joint ◽  
Heavy Duty ◽  
Comprehensive Overview ◽  
Vibration Signals ◽  
Nonlinear Dynamical ◽  
Rolling Mills ◽  
Transient Oscillations

The problem solved in this research is the diagnosis of the radial clearances in bearing supports and the loosening of fastening bolts due to their plastic elongation (creep) or weak tightening using vibration signals. This is an important issue for the maintenance of the heavy-duty gearboxes of powerful mining machines and rolling mills working in non-stationary regimes. Based on a comprehensive overview of bolted joint diagnostic methods, a solution to this problem based on a developed nonlinear dynamical model of bearing supports is proposed. Diagnostic rules are developed by comparing the changes of natural frequency and its harmonics, the amplitudes and phases of shaft transient oscillations. Then, the vibration signals are measured on real gearboxes while the torque is increasing in the transmission during several series of industrial trials under changing bearings and bolts conditions. In parallel, dynamical torque is measured and its interrelation with vibration is determined. It is concluded that the radial clearances are the most influencing factors among the failure parameters in heavy-duty gearboxes of industrial machines working under impulsive and step-like loading. The developed diagnostics algorithm allows condition monitoring of bearings and fastening bolts, allowing one to undertake timely maintenance actions to prevent failures.

scholarly journals Condition Monitoring of Blade in Turbomachinery: A Review

2014 ◽  
Vol 6 ◽  
pp. 210717 ◽  
Author(s):  
Ahmed M. Abdelrhman ◽  
Lim Meng Hee ◽  
M. S. Leong ◽  
Salah Al-Obaidi
Keyword(s):  
Condition Monitoring ◽  
Fatigue Failure ◽  
Signal Analysis ◽  
Health Condition ◽  
Diagnostic Methods ◽  
Monitoring Techniques ◽  
Analysis Methods

Blade faults and blade failures are ranked among the most frequent causes of failures in turbomachinery. This paper provides a review on the condition monitoring techniques and the most suitable signal analysis methods to detect and diagnose the health condition of blades in turbomachinery. In this paper, blade faults are categorised into five types in accordance with their nature and characteristics, namely, blade rubbing, blade fatigue failure, blade deformations (twisting, creeping, corrosion, and erosion), blade fouling, and loose blade. Reviews on characteristics and the specific diagnostic methods to detect each type of blade faults are also presented. This paper also aims to provide a reference in selecting the most suitable approaches to monitor the health condition of blades in turbomachinery.

Comparison of Linear and Non-Linear Gas Turbine Performance Diagnostics

2003 ◽  
Author(s):  
Ph. Kamboukos ◽  
K. Mathioudakis
Keyword(s):  
Gas Turbine ◽  
Condition Monitoring ◽  
Theoretical Background ◽  
Diagnostic Methods ◽  
Turbine Performance ◽  
Different Types ◽  
Non Linear ◽  
Computational Procedures ◽  
Monitoring And Diagnostics ◽  
Performance Diagnostics

The features of linear performance diagnostic methods are discussed, in comparison to methods based on full non-linear calculation of performance deviations, for the purpose of condition monitoring and diagnostics. First, the theoretical background of linear methods is overviewed to establish a relationship to the principles used by non-linear methods. Then computational procedures are discussed and compared. The effectiveness of determining component performance deviations by the two types of approaches is examined, on different types of diagnostic situations. A way of establishing criteria to define whether non-linear methods have to be employed is presented. An overall assessment of merits or weaknesses of the two types of methods is attempted, based on the results presented in the paper.

Residual-based fault detection using soft computing techniques for condition monitoring at rolling mills

2014 ◽  
Vol 259 ◽  
pp. 304-320 ◽  
Author(s):  
Francisco Serdio ◽  
Edwin Lughofer ◽  
Kurt Pichler ◽  
Thomas Buchegger ◽  
Hajrudin Efendic
Keyword(s):  
Fault Detection ◽  
Soft Computing ◽  
Condition Monitoring ◽  
Rolling Mills ◽  
Soft Computing Techniques

Bearing Scuffing Detection and Condition Monitoring Using Virtual Instrumentation

2014 ◽  
Vol 657 ◽  
pp. 604-608 ◽  
Author(s):  
Carmen Bujoreanu ◽  
Florin Breabăn
Keyword(s):  
Condition Monitoring ◽  
Virtual Instrument ◽  
Friction Torque ◽  
Diagnostic Methods ◽  
Virtual Instrumentation ◽  
Friction Forces ◽  
Data Acquisition Board ◽  
Bearing Condition Monitoring ◽  
Braking Torque ◽  
Vibration Signature

Bearing condition monitoring confronts the most machine users. Diagnostic methods used to include bearing problems represent one of the most important challenges. The scuffing phenomenon initiation of the bearing elements produces an important increase in the vibration level and can be emphasized by the analysis of the bearing friction forces which are the most sensitive indicator of the bearing failure. Commonly used technique for damage detection is the vibration signature analysis that must be carefully utilized in conjunction with the friction torque monitoring through the strain gauges measurements. In order to detect the scuffing onset, the paper presents an experimental setup for the scuffing tests performed on a 7206 ball bearing. A virtual instrument monitoring the friction force respectively the braking torque was created. An accelerometer captures the signal from the bearing outer ring then it is processed using PCI-4451 National Instruments data acquisition board and LabVIEW soft.

scholarly journals Frequency and Time-Frequency Analysis of Cutting Force and Vibration Signals for Tool Condition Monitoring

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 6400-6410 ◽  
Author(s):  
Juan C. Jauregui ◽  
Juvenal R. Resendiz ◽  
Suresh Thenozhi ◽  
Tibor Szalay ◽  
Adam Jacso ◽  
...  
Keyword(s):  
Cutting Force ◽  
Condition Monitoring ◽  
Frequency Analysis ◽  
Tool Condition Monitoring ◽  
Vibration Signals ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
Tool Condition

Evaluation of Diagnostic Performance for the Gear Box Considering the Measurement Position Effect With Wavelet Packet Transform

2009 ◽  
Author(s):  
Young-Sun Hong ◽  
Gil-Yong Lee ◽  
Young-Man Cho ◽  
Sung-Hoon Ahn ◽  
Chul-Ki Song
Keyword(s):  
Wavelet Packet ◽  
Position Effect ◽  
Vibration Signal ◽  
Industrial Applications ◽  
Wavelet Packet Transform ◽  
Bevel Gear ◽  
Diagnostic Methods ◽  
Vibration Signals ◽  
Rotary Machinery ◽  
Fault Features

There has been much research into monitoring techniques for mechanical systems to ensure stable production levels in modern industries. This is particularly true for the diagnostic monitoring of rotary machinery, because faults in this type of equipment appear frequently and quickly cause severe problems. Such diagnostic methods are often based on the analysis of vibration signals because they are directly related to physical faults. Even though the magnitude of vibration signals depends on the measurement position, the effect of measurement position is generally not considered. This paper describes an investigation of the effect of the measurement position on the fault features in vibration signals. The signals for normal and broken bevel gears were measured at the base, gearbox, and bevel gear, simultaneously, of a machine fault simulator (MFS). These vibration signals were compared to each other and used to estimate the classification efficiency of a diagnostic method using wavelet packet transform. From this experiment, the fault features are more prominently in the vibration signal from the measurement position of the bevel gear than from the base and gearbox. The results of this analysis will assist in selecting the appropriate measurement position in real industrial applications and precision diagnostics.

Multi-fault Condition Monitoring of Slurry Pump with Principle Component Analysis and Sequential Hypothesis Test

2019 ◽  
Vol 34 (07) ◽  
pp. 2059019 ◽  
Author(s):  
Hanxin Chen ◽  
Wenjian Huang ◽  
Jinmin Huang ◽  
Chenghao Cao ◽  
Liu Yang ◽  
...  
Keyword(s):  
Condition Monitoring ◽  
Hypothesis Test ◽  
Wavelet Packet ◽  
Principal Component ◽  
Vibration Signal ◽  
Component Analysis ◽  
Ratio Test ◽  
Vibration Signals ◽  
Time Domain Signal ◽  
Sequential Probability

A new method about the multi-fault condition monitoring of slurry pump based on principal component analysis (PCA) and sequential probability ratio test (SPRT) is proposed. The method identifies the condition of the slurry pump by analyzing the vibration signal. The experimental model is established using the normal impeller and the faulty impellers where the collected vibration signals were preprocessed using wavelet packet transform (WPT). The characteristic parameters of the vibration signals are extracted by time domain signal analysis and the dimension of data was reduced by PCA. The principal components with the largest contribution rate are chosen as the inputted signal to SPRT to assess the proposed algorithm. The new methodology is reasonable and practical for the multi-fault diagnosis of slurry pump.

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