scholarly journals Using Simplified Models to Assist Fault Detection and Diagnosis in Large Hydrogenerators

2017 ◽  
Vol 2017 ◽  
pp. 1-18
Author(s):  
Geraldo Carvalho Brito Junior ◽  
Roberto Dalledone Machado ◽  
Anselmo Chaves Neto
Keyword(s):  
Degrees Of Freedom ◽  
Thickness Distribution ◽  
Accurate Determination ◽  
Fault Detection And Diagnosis ◽  
Operating Conditions ◽  
Vibration Monitoring ◽  
Simplified Models ◽  
Detection And Diagnosis ◽  
Hydrodynamic Effects

Based on experimental evidence collected in a set of twenty 700 MW hydrogenerators, this article shows that the operating conditions of large hydrogenerators journal bearings may have unpredictable and significant changes, without apparent reasons. These changes prevent the accurate determination of bearing dynamic coefficients and make the prediction of these machines dynamic behavior unfeasible, even using refined models. This makes it difficult to differentiate the normal changes in hydrogenerators dynamics from the changes created by a fault event. To overcome such difficulty, this article proposes a back-to-basics step, the using of simplified mathematical models to assist hydrogenerators vibration monitoring and exemplifies this proposal by modeling a 700 MW hydrogenerator. A first model estimates the influence of changes in bearing operating conditions in the bearing stiffnesses, considering only the hydrodynamic effects of an isoviscous oil film with linear thickness distribution. A second model simulates hydrogenerators dynamics using only 10 degrees of freedom, giving the monitored vibrations as outputs, under normal operating conditions or in the presence of a fault. This article shows that simplified models may give satisfactory results when bearing operating conditions are properly determined, results comparable to those obtained by more refined models or by measurements in the modeled hydrogenerator.

An Experimental Analysis of Misalignment Effects on Hydrodynamic Plain Journal Bearing Performances

2001 ◽  
Vol 124 (2) ◽  
pp. 313-319 ◽  
Author(s):  
J. Bouyer ◽  
M. Fillon
Keyword(s):  
Film Thickness ◽  
Journal Bearing ◽  
Hydrodynamic Pressure ◽  
Operating Conditions ◽  
Temperature Fields ◽  
Maximum Pressure ◽  
Minimum Film Thickness ◽  
Oil Flow ◽  
Hydrodynamic Effects

The present study deals with the experimental determination of the performance of a 100 mm diameter plain journal bearing submitted to a misalignment torque. Hydrodynamic pressure and temperature fields in the mid-plane of the bearing, temperatures in two axial directions, oil flow rate, and minimum film thickness, were all measured for various operating conditions and misalignment torques. Tests were carried out for rotational speeds ranging from 1500 to 4000 rpm with a maximum static load of 9000 N and a misalignment torque varying from 0 to 70 N.m. The bearing performances were greatly affected by the misalignment. The maximum pressure in the mid-plane decreased by 20 percent for the largest misalignment torque while the minimum film thickness was reduced by 80 percent. The misalignment caused more significant changes in bearing performance when the rotational speed or load was low. The hydrodynamic effects were then relatively small and the bearing offered less resistance to the misalignment.

Parametric Optimization of Structures Under Combined Base Motion Direct Forces and Static Loading

1998 ◽  
Vol 124 (1) ◽  
pp. 132-140 ◽  
Author(s):  
Izhak Bucher
Keyword(s):  
Degrees Of Freedom ◽  
Structural Parameters ◽  
Operating Conditions ◽  
Design Parameters ◽  
Power Spectral ◽  
Loading Patterns ◽  
The Cost ◽  
Selection Of ◽  
Eigenvectors And Eigenvalues

This paper deals with the optimization of vibrating structures as a mean for minimizing unwanted vibration. Presented in this work is a method for automatic determination of a set of preselected design parameters affecting the geometrical layout or shape of the structure. The parameters are selected to minimize the dynamic response to external forcing or base motion. The presented method adjusts the structural parameters by solving an optimization problem in which the constraints are dictated by engineering considerations. Several constraints are defined so that the static deflection, the stress levels and the total weight of the structure are kept within bounds. The dynamic loading acting upon the structure is described in this work by its power spectral density, with this representation the structure can be tailored to specific operating conditions. The uncertain nature of the excitation is overcome by combining all possible spectra into one PSD encompassing all possible loading patterns. An important feature of the presented method is its numerical efficiency. This feature is essential for any reasonably sized problem as such problems are usually described by thousands of degrees of freedom arising from a finite-element idealization of the structure. In this paper, efficient, closed form expressions, for the cost function and its gradients are derived. Those are computed with a partial set of eigenvectors and eigenvalues thus increasing the efficiency further. Several numerical examples are presented where both shape optimization and the selection of discrete components are illustrated.

A Model-Based Online Fault Detection Method for Air Handling Units of Real Office Buildings

2011 ◽  
Vol 90-93 ◽  
pp. 3061-3067
Author(s):  
Hai Tao Wang ◽  
You Ming Chen ◽  
Cary W.H. Chan ◽  
Jian Ying Qin
Keyword(s):  
Fault Detection ◽  
Detection Method ◽  
Fault Detection And Diagnosis ◽  
Operating Conditions ◽  
Office Buildings ◽  
Model Parameters ◽  
Time Data ◽  
Model Based ◽  
Air Handling Units ◽  
Detection And Diagnosis

The increasing performance demands and the growing complexity of heating, ventilation and air conditioning (HVAC) systems have created a need for automated fault detection and diagnosis (FDD) tools. Cost-effective fault detection and diagnosis method is critical to develop FDD tools. To this end, this paper presents a model-based online fault detection method for air handling units (AHU) of real office buildings. The model parameters are periodically adjusted by a genetic algorithm-based optimization method to reduce the residual between measured and predicted data, so high modeling accuracy is assured. If the residual between measured and estimated performance data exceeds preset thresholds, it means the occurrence of faults or abnormalities in the air handling unit system. In addition, an online adaptive scheme is developed to estimate and update the thresholds, which vary with system operating conditions. The model-based fault detection method needs no additional instrumentation in implementation and can be easily integrated with existing energy management and control systems (EMCS). The fault detection method was tested and validated using in real time data collected from a real office building.

scholarly journals Fuzzy Fault Detection for Permanent Magnet Synchronous Motor

2021 ◽  
Vol 346 ◽  
pp. 03067
Author(s):  
Alexander Romanov
Keyword(s):  
Fault Detection ◽  
Permanent Magnets ◽  
Residual Life ◽  
Permanent Magnet Synchronous Motor ◽  
Synchronous Motor ◽  
Fault Detection And Diagnosis ◽  
Operating Conditions ◽  
Detection Scheme ◽  
Synchronous Motors ◽  
Detection And Diagnosis

In the transition to automated and automatic manufacturing an urgent problem is to increase the reliability of mobile robots (MR) and their drives, creation of devices to monitor the technical characteristics of MR, diagnose and predict the remaining resource. Inspite of the high relevance of the diagnosing MR drives problem, there are no generally accepted methodology for diagnosing MR drives, criteria for selecting methods, parameters and volumes of diagnostics at present. An unsolved problem, related to the diagnosis of MR drives and the prediction of their residual life remains, is the development of methods that allow to carry out of automatic complex multiparametric diagnostics and prediction of the residual life using artificial intelligence methods. Effective fault detection and diagnosis can improve the reliability of the MR drive and avoid costly maintenance. In this paper a fault detection scheme for synchronous motors with permanent magnets based on a fuzzy system is proposed. The sequence current components (positive and negative sequence currents) are used as fault indicators and are set as input to the fuzzy fault detector. The expediency of the proposed scheme for determining of various types of faults for a synchronous motor with permanent magnets under various operating conditions is simulated using the SimInTech software.

Model-based diagnosis of electronic cooling fan drive systems

2018 ◽  
Author(s):  
Michael Pagel
Keyword(s):  
Fault Detection ◽  
Fault Detection And Diagnosis ◽  
Equivalent Series Resistance ◽  
Efficient Design ◽  
Model Based ◽  
Cooling Fan ◽  
Internal States ◽  
Detection And Diagnosis ◽  
Drive Systems

Kurzzusammenfassung: Model-based diagnosis of electric cooling fan drive systems is a contribution to the field of fault detection and diagnosis for electrically driven engine cooling fans. Its main focus is on the online gathering and determination of important parameters and internal states. The developed methods for fault detection and diagnosis are characterized by resource and computing efficient design and by a low application effort, drastically reducing the costs for transferring them to other applications. Novel algorithms are presented for determination of the winding resistance, the flux linkage over angle and the equivalent series resistance. Based on these algorithms, a new and innovative approach for determination of the magnet temperature is proposed, utilizing the winding temperature, which is derived without requiring an additional temperature sensor. Furthermore, methods are presented for detection of a demagnetization event, detection of an aged DC-link capacitor and...

scholarly journals Real Time Monitoring and Neuro-Fuzzy Based Fault Diagnosis of Flow Process in Hybrid System

2012 ◽  
pp. 171-177
Author(s):  
Revathi. P ◽  
Pallikonda Rajasekaran. M ◽  
Babiyola. D ◽  
Aruna. R
Keyword(s):  
Neural Network ◽  
Fault Detection ◽  
Real Time ◽  
Fault Detection And Diagnosis ◽  
Operating Conditions ◽  
Economic Losses ◽  
Monitoring Systems ◽  
Back Propagation Algorithm ◽  
System Failures ◽  
Detection And Diagnosis

Process variables vary with time in certain applications. Monitoring systems let us avoid severe economic losses resulting from unexpected electric system failures by improving the system reliability and maintainability The installation and maintenance of such monitoring systems is easy when it is implemented using wireless techniques. ZigBee protocol, that is a wireless technology developed as open global standard to address the low-cost, low-power wireless sensor networks. The goal is to monitor the parameters and to classify the parameters in normal and abnormal conditions to detect fault in the process as early as possible by using artificial intelligent techniques. A key issue is to prevent local faults to be developed into system failures that may cause safety hazards, stop temporarily the production and possible detrimental environment impact. Several techniques are being investigated as an extension to the traditional fault detection and diagnosis. Computational intelligence techniques are being investigated as an extension to the traditional fault detection and diagnosis methods. This paper proposes ANFIS (Adaptive Neural Fuzzy Inference System) for fault detection and diagnosis. In ANFIS, the fuzzy logic will create the rules and membership functions whereas the neural network trains the membership function to get the best output. The output of ANFIS is compared with Back Propagation Algorithm (BPN) algorithm of neural network. The training and testing data required to develop the ANFIS model were generated at different operating conditions by running the process and by creating various faults in real time in a laboratory experimental model.

scholarly journals Fault Detection of Inline Reciprocating Diesel Engine: A Mass and Gas-Torque Approach

2012 ◽  
Vol 2012 ◽  
pp. 1-6 ◽  
Author(s):  
S. H. Gawande ◽  
L. G. Navale ◽  
M. R. Nandgaonkar ◽  
D. S. Butala ◽  
S. Kunamalla
Keyword(s):  
Diesel Engine ◽  
Fault Detection ◽  
Constant Load ◽  
Gas Pressure ◽  
Fault Detection And Diagnosis ◽  
Operating Conditions ◽  
Medium Speed ◽  
Di Diesel Engine ◽  
Detection And Diagnosis ◽  
Early Fault Detection

Early fault detection and diagnosis for medium-speed diesel engines are important to ensure reliable operation throughout the course of their service. This work presents an investigation of the diesel engine combustion-related fault detection capability of crankshaft torsional vibrations. Proposed methodology state the way of early fault detection in the operating six-cylinder diesel engine. The model of six cylinders DI Diesel engine is developed appropriately. As per the earlier work by the same author the torsional vibration amplitudes are used to superimpose the mass and gas torque. Further mass and gas torque analysis is used to detect fault in the operating engine. The DFT of the measured crankshaft’s speed, under steady-state operating conditions at constant load shows significant variation of the amplitude of the lowest major harmonic order. This is valid both for uniform operating and faulty conditions and the lowest harmonic orders may be used to correlate its amplitude to the gas pressure torque and mass torque for a given engine. The amplitudes of the lowest harmonic orders (0.5, 1, and 1.5) of the gas pressure torque and mass torque are used to map the fault. A method capable to detect faulty cylinder of operating Kirloskar diesel engine of SL90 Engine-SL8800TA type is developed, based on the phases of the lowest three harmonic orders.

Design of a Wireless Sensor Node for Vibration Monitoring of Industrial Machinery

2016 ◽  
Vol 6 (2) ◽  
pp. 639 ◽  
Author(s):  
Alaa Abdulhady Jaber ◽  
Robert Bicker
Keyword(s):  
Fault Detection ◽  
Condition Monitoring ◽  
Industrial Robot ◽  
Industrial Applications ◽  
Base Station ◽  
Fault Detection And Diagnosis ◽  
Vibration Monitoring ◽  
Wireless Sensor Node ◽  
Vibration Signal Analysis ◽  
Detection And Diagnosis

Machine healthy monitoring is a type of maintenance inspection technique by which an operational asset is monitored and the data obtained is analysed to detect signs of degradation, diagnose the causes of faults and thus reducing the maintenance costs. Vibration signals analysis was extensively used for machines fault detection and diagnosis in various industrial applications, as it respond immediately to manifest itself if any change is appeared in the monitored machine. However, recent developments in electronics and computing have opened new horizons in the area of condition monitoring and have shown their practicality in fault detection and diagnosis processes. The main aim of using wireless embedded systems is to allow data analysis to be carried out locally at field level and transmitting the results wirelessly to the base station, which as a result will help to overcome the need for wiring and provides an easy and cost-effective sensing technique to detect faults in machines. So, the main focuses of this research is to design and develop an online condition monitoring system based on wireless embedded technology that can be used to detect and diagnose the most common faults in the transmission systems (gears and bearings) of an industrial robot joints using vibration signal analysis.

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