scholarly journals Condition Monitoring of Forward Curved Centrifugal Blower Using Coast Down Time Analysis

2010 ◽  
Vol 2010 ◽  
pp. 1-12
Author(s):  
G. R. Rameshkumar ◽  
B. V. A. Rao ◽  
K. P. Ramachandran
Keyword(s):  
Condition Monitoring ◽  
Health Condition ◽  
Centrifugal Blower ◽  
Rotating Machine ◽  
Monitoring Parameter ◽  
Shaft Misalignment ◽  
Sustained Operation ◽  
Mechanical Faults ◽  
Machine Health ◽  
Reduction Percentage

Mechanical malfunctions such as, rotor unbalance and shaft misalignment are the most common causes of vibration in rotating machineries. Vibration is the most widely used parameter to monitor and asses the machine health condition. In this work, the Coast Down Time (CDT), which is an indicator of faults, is used to assess the condition of the rotating machine as a condition monitoring parameter. CDT is the total time taken by the system to dissipate the momentum acquired during sustained operation. Extensive experiments were conducted on Forward Curved Centrifugal Blower Test Rig at selected cutoff speeds for several combinations of combined horizontal and vertical parallel misalignment, combined parallel and angular misalignment, as well as for various unbalance conditions. As mechanical faults increase, a drastic decrease in CDT is found and this is represented as CDT reduction percentage. A specific correlation between the CDT reduction percentage, level of mechanical faults, and rotational cutoff speeds is observed. The results are analyzed and compared with vibration analysis for potential use of CDT as one of the condition monitoring parameter.

CDT Analysis is Used as a Condition Monitoring Parameter to Study the Tribological Behavior of Bearing Lubricant Under Different Operating Pressure

2006 ◽  
Author(s):  
R. Edwin Browne ◽  
K. P. Ramachandran ◽  
A. K. M. De Silva ◽  
D. K. Harrison
Keyword(s):  
Condition Monitoring ◽  
Journal Bearing ◽  
Tribological Behavior ◽  
Input Function ◽  
Rotor System ◽  
Operating Pressure ◽  
Tribological Behaviour ◽  
Rotating System ◽  
Monitoring Parameter ◽  
Sustained Operation

The deceleration and running down operation of any rotating system when analysed would lead to useful input function for condition monitoring. When the power supply to any rotor system is cut off, total momentum gained through the sustained operation will dissipate and the system will come to halt. The time elapsed between the power cutoff and the complete stop of the system is defined as CDT (Coast Down Time). The tribological behaviour could be effectively monitored through this parameter (CDT), which has been conducted on a horizontal rotor system with a full journal bearing at different speed and lubrication pressures. The characteristics are compared with steady state vibration in order to emphasize the potential benefit of using CDT as a useful diagnostic parameter for condition monitoring.

EMD, Ranking Mutual Information and PCA Based Condition Monitoring

2010 ◽  
Author(s):  
Xiaomin Zhao ◽  
Ming J. Zuo ◽  
Tejas Patel
Keyword(s):  
Mutual Information ◽  
Condition Monitoring ◽  
Feature Fusion ◽  
Health Condition ◽  
Multidimensional Analysis ◽  
Analysis Tool ◽  
Monitoring Method ◽  
Health Monitoring System ◽  
Monotonic Trend ◽  
Monitoring Parameter

Success of any health monitoring system chiefly relies on the effectiveness of condition monitoring parameter. The parameter could be a single or combination of many vibration features. These features are expected to have a monotonic trend with the damage/fault progression. Ranking mutual information technique has the ability to detect the features that have monotonic trend and PCA is a popular and widely accepted multidimensional analysis tool for the feature fusion. A condition monitoring method is presented in this paper by combining EMD, ranking mutual information and PCA. The proposed method is helpful in generation of the indicator that represents the damage progression. This method is tested on the impeller health condition monitoring of a pump.

Automatic Condition Monitoring and Early Fault Detection of Gearbox

2009 ◽  
Author(s):  
Dong Wang ◽  
Qiang Miao ◽  
Chengdong Wang ◽  
Jingqi Xiong
Keyword(s):  
Condition Monitoring ◽  
Wavelet Packet ◽  
Life Time ◽  
Health Condition ◽  
Evaluation Index ◽  
Dynamic Threshold ◽  
Time Data ◽  
Machinery Condition Monitoring ◽  
Maintenance Program ◽  
Gear Fault

Condition based maintenance (CBM) improves decision-making performances for a maintenance program through machinery condition monitoring. Therefore, it is a key step to trace machinery health condition for CBM. In this paper, a novel method is proposed to establish a health evaluation index named automatic evaluation index (AEI) and its corresponding dynamic threshold using Wavelet Packet Transform (WPT) and Hidden Markolv Model (HMM). In this process, WPT is used to decompose signal into detail signals and exhibits prominent gear fault features. In addition, HMM employed here is to recognize two concerned states of gear in the whole life validation, including normal gear state and early gear fault state. It is also important to build a dynamic threshold to differentiate the two states automatically. The proposed dynamic threshold not only renews by itself according to the history values of AEI but also easily and automatically detects occurrence of gear early fault. Finally, a set of whole life time data ending in gear failure is used to verify the proposed method effectively. Further, some related parameters included in this method are discussed and the obtained results show that condition monitoring performance of the proposed method is excellent in detection of gear failure.

Modeling and Parameter Identification for Condition Monitoring of Surface-Mount Permanent Magnet Machines Under Magnet Demagnetization

2020 ◽  
Author(s):  
Fanny Pinto Delgado ◽  
Ziyou Song ◽  
Heath F. Hofmann ◽  
Jing Sun
Keyword(s):  
Permanent Magnet ◽  
Condition Monitoring ◽  
High Performance ◽  
High Efficiency ◽  
Health Condition ◽  
Maintenance Cost ◽  
Parameter Estimator ◽  
Two Phase ◽  
Machine Model ◽  
Surface Mount

Abstract Permanent Magnet Synchronous Machines (PMSMs) have been preferred for high-performance applications due to their high torque density, high power density, high control accuracy, and high efficiency over a wide operating range. During operation, monitoring the PMSM’s health condition is crucial for detecting any anomalies so that performance degradation, maintenance/downtime costs, and safety hazards can be avoided. In particular, demagnetization of PMSMs can lead to not only degraded performance but also high maintenance cost as they are the most expensive components in a PMSM. In this paper, an equivalent two-phase model for surface-mount permanent magnet (SMPM) machines under permanent magnet demagnetization is formulated and a parameter estimator is proposed for condition monitoring purposes. The performance of the proposed estimator is investigated through analysis and simulation under different conditions, and compared with a parameter estimator based on the standard SMPM machine model. In terms of information that can be extracted for fault diagnosis and condition monitoring, the proposed estimator exhibits advantages over the standard-model-based estimator as it can differentiate between uniform demagnetization over all poles and asymmetric demagnetization between north and south poles.

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.

scholarly journals Vestas V90-3MW Wind Turbine Gearbox Health Assessment Using a Vibration-Based Condition Monitoring System

2016 ◽  
Vol 2016 ◽  
pp. 1-18 ◽  
Author(s):  
A. Romero ◽  
Y. Lage ◽  
S. Soua ◽  
B. Wang ◽  
T.-H. Gan
Keyword(s):  
Fault Diagnosis ◽  
Wind Turbine ◽  
Condition Monitoring ◽  
Learning Algorithm ◽  
Health Assessment ◽  
Field Trials ◽  
Health Condition ◽  
Normal Operation ◽  
Wind Turbine Gearbox ◽  
Operation Conditions

Reliable monitoring for the early fault diagnosis of gearbox faults is of great concern for the wind industry. This paper presents a novel approach for health condition monitoring (CM) and fault diagnosis in wind turbine gearboxes using vibration analysis. This methodology is based on a machine learning algorithm that generates a baseline for the identification of deviations from the normal operation conditions of the turbine and the intrinsic characteristic-scale decomposition (ICD) method for fault type recognition. Outliers picked up during the baseline stage are decomposed by the ICD method to obtain the product components which reveal the fault information. The new methodology proposed for gear and bearing defect identification was validated by laboratory and field trials, comparing well with the methods reviewed in the literature.

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