Fault Diagnostic of Electro-Hydraulic Servo Valve

2012 ◽  
Vol 619 ◽  
pp. 259-263
Author(s):  
Hai Yang Pan ◽  
Shu Nan Liu ◽  
Yong Ming Yao ◽  
Yi Lin Jiang
Keyword(s):  
Physical Model ◽  
Failure Modes ◽  
Health Management ◽  
Data Driven ◽  
Servo Valve ◽  
Data Driven Approach ◽  
Hydraulic Servo ◽  
Simulation Results ◽  
Fault Diagnostic ◽  
Prognostic And Health Management

In order to achieve the fault diagnostic and degradation assessment of the electro-hydraulic servo valve, a system of prognostic and health management based on the data-driven approach for the electro-hydraulic servo valve is presented. FMMEA performed in this study considered the degree of sub-components in electro-hydraulic servo valve. In order to use only five parameters to assess the cause of degradation, a physical model of the EH servo valve was built up to simulate the failure modes. The simulation results are very useful because the methods can be applied to assess the cause of degradation such as leakage, jamming, clogging and so on.

Using Integration of Thermal-Balance Based and Data-Driven Models to Determine Single Duct Variable Air Volume System Cooling Baseline

2013 ◽  
Author(s):  
Gyujin Shim ◽  
Li Song ◽  
Gang Wang
Keyword(s):  
Physical Model ◽  
Least Square Method ◽  
Energy Performance ◽  
Least Square ◽  
Physical Models ◽  
Data Driven ◽  
Lumped Model ◽  
Variable Air Volume ◽  
Data Driven Approach ◽  
Cooling Coil

In order to use real-time energy measurements to identify system operation faults and inefficiencies, a cooling coil energy baseline is studied in an air-handling unit (AHU) through an integration of physical models and a data driven approach in this paper. A physical model for an AHU cooling coil energy consumption is first built to understand equipment mechanism and to determine the variables impacting cooling coil energy performance, and then the physical model is simplified into a lumped model by reducing the number of independent variables needed. Regression coefficients in the lumped model are determined statistically through searching optimal fit using the least square method with short periods of measured data. Experimental results on an operational AHU (8 ton) are presented to validate the effectiveness of this approach with statistical analysis. As a result of this experiment, the proposed cooling energy baselines at the cooling coil have ±20% errors at 99.7% confidence. Six-day data for obtaining baseline is preferred since it shows similar results as 12-day.

Research on Servo Valve-Controlled Motor Velocity Control System Based on Fuzzy Control

2013 ◽  
Vol 345 ◽  
pp. 77-80
Author(s):  
Yi Juan Zang
Keyword(s):  
Adverse Effect ◽  
Fuzzy Control ◽  
Motor System ◽  
The Self ◽  
Velocity Control ◽  
Servo Valve ◽  
Hydraulic Servo ◽  
Simulation Results ◽  
Adaptive Ability ◽  
Motor Velocity

This paper made an in-depth search about the motor rotate speed control which was combined with fuzzy control. The simulation results show that the adverse effect for load moment disturbance is reduced, the self-adaptive ability and controlling performance of the hydraulic servo valve-controlled motor system are improved.

Prognostics of Embedded Planar Capacitors Under Temperature and Voltage Aging

2010 ◽  
Author(s):  
Mohammed A. Alam ◽  
Michael H. Azarian ◽  
Michael Osterman ◽  
Michael Pecht
Keyword(s):  
Elevated Temperature ◽  
Mahalanobis Distance ◽  
Failure Modes ◽  
Gradual Decrease ◽  
Data Driven ◽  
Time To Failure ◽  
Insulation Resistance ◽  
Sharp Drop ◽  
Model Based ◽  
Data Driven Approach

This paper presents the application of model-based and data-driven approaches for prognostics and health management (PHM) of embedded planar capacitors under elevated temperature and voltage conditions. An embedded planar capacitor is a thin laminate that serves both as a power/ground plane and as a parallel plate capacitor in a multilayered printed wiring board (PWB). These capacitors are typically used for decoupling applications and are found to reduce the required number of surface mount capacitors. The capacitor laminate used in this study consisted of an epoxy-barium titanate (BaTiO3) composite dielectric sandwiched between Cu layers. Three electrical parameters, capacitance, dissipation factor, and insulation resistance, were monitored in-situ once every hour during testing under elevated temperature and voltage aging conditions. The failure modes observed were a sharp drop in insulation resistance and a gradual decrease in capacitance. An approach to model the time-to-failure associated with these failure modes as a function of the stress level is presented in this paper. Model-based PHM can be used to predict the time-to-failure associated with a single failure mode, consisting of a drop in either insulation resistance or capacitance. However, failure of an embedded capacitor could occur due to either of these two failure modes and was not captured using a single model. A combined model for both these failure modes can be developed but there was a large variance in the time-to-failure data of failures as a result of a sharp drop in insulation resistance. Therefore a data-driven approach, which utilizes the trend and correlation between the parameters to predict remaining life, was investigated to perform PHM. The data-driven approach used in this paper is the Mahalanobis distance (MD) method that reduces a multivariate data set to a single parameter by considering correlations among the parameters. The Mahalanobis distance method was successful in predicting the failures as a result of a gradual decrease in capacitance. However, prediction of failures as a result of a drop in insulation resistance was generally challenging due to their sudden onset. An experimental approach to address such sudden failures is discussed to facilitate identifying any trends in the parameters prior to failure.

scholarly journals Data-driven Application of PHM to Asset Strategies

2018 ◽  
Vol 10 (1) ◽  
Author(s):  
Sarah Lukens ◽  
Matt Markham
Keyword(s):  
Data Analytics ◽  
Failure Modes ◽  
Health Management ◽  
Data Driven ◽  
Work Processes ◽  
Industrial Companies ◽  
Use Of Data ◽  
Reliability Centered Maintenance ◽  
Daunting Challenge

There are many benefits from implementing a prognostics and health management (PHM) initiative in an industrial facility, such as realizing potentials from reducing unplanned downtime and increased asset efficiency. Many industrial companies would like to take advantage of PHM technologies and algorithms to meet their business objectives, but identifying how to get started can be a daunting challenge. The classical approach is to begin with a Reliability Centered Maintenance (RCM) program supported by failure modes and effects analysis (FMEA) where all possible failure modes, their risks, and mitigating actions are evaluated in the context of asset function. In this framework, application of PHM technologies is viewed as a maintenance strategy effective at mitigating certain failure modes in specific cases that are both feasible and costeffective. However, there are many challenges and limitations to traditional RCM where data-driven analytics embedded in these work processes can help overcome and/or automate. On the other hand, the use of data-driven approaches introduces new challenges surrounding available data, data quality, and identifying numerical methods that are scalable across large datasets. In this paper, we present a case study applied to historical maintenance data for identifying and prioritizing where to start a PHM initiative, and discuss the work processes and various challenges encountered when embedding data analytics in classical reliability approaches.

scholarly journals A Review of Data-Driven Oil and Gas Pipeline Pitting Corrosion Growth Models Applicable for Prognostic and Health Management

2020 ◽  
Vol 9 (1) ◽  
Author(s):  
Roohollah Heidary ◽  
Steven A. Gabriel ◽  
Mohammad Modarres ◽  
Katrina M. Groth ◽  
Nader Vahdati
Keyword(s):  
Stochastic Process ◽  
Pitting Corrosion ◽  
Oil And Gas ◽  
Health Management ◽  
Assessment Model ◽  
Data Driven ◽  
Gas Pipelines ◽  
Oil And Gas Pipelines ◽  
Process Based Models ◽  
Prognostic And Health Management

Pitting corrosion is a primary and most severe failure mechanism of oil and gas pipelines. To implement a prognostic and health management (PHM) for oil and gas pipelines corroded by internal pitting, an appropriate degradation model is required. An appropriate and highly reliable pitting corrosion degradation assessment model should consider, in addition to epistemic uncertainty, the temporal aspects, the spatial heterogeneity, and inspection errors. It should also take into account the two well-known characteristics of pitting corrosion growing behavior: depth and time dependency of pit growth rate. Analysis of these different levels of uncertainties in the amount of corrosion damage over time should be performed for continuous and failure-free operation of the pipelines. This paper reviews some of the leading probabilistic data-driven prediction models for PHM analysis for oil and gas pipelines corroded by internal pitting. These models categorized as random variable-based and stochastic process-based models are reviewed and the appropriateness of each category is discussed. Since stochastic process-based models are more versatile to predict the behavior of internal pitting corrosion in oil and gas pipelines, the capabilities of the two popular stochastic process-based models, Markov process-based and gamma process-based, are discussed in more detail.

scholarly journals A Data-Driven Approach to Extend Failure Analysis: A Framework Development and a Case Study on a Hydroelectric Power Plant

Energies ◽  
2020 ◽  
Vol 13 (23) ◽  
pp. 6400
Author(s):  
Sara Antomarioni ◽  
Marjorie Maria Bellinello ◽  
Maurizio Bevilacqua ◽  
Filippo Emanuele Ciarapica ◽  
Renan Favarão da Silva ◽  
...  
Keyword(s):  
Power Plant ◽  
Failure Analysis ◽  
Power Plants ◽  
Failure Modes ◽  
Real Life ◽  
Hydroelectric Power Plant ◽  
Data Driven ◽  
Hydroelectric Power ◽  
Data Driven Approach

Power plants are required to supply the electric demand efficiently, and appropriate failure analysis is necessary for ensuring their reliability. This paper proposes a framework to extend the failure analysis: indeed, the outcomes traditionally carried out through techniques such as the Failure Mode and Effects Analysis (FMEA) are elaborated through data-driven methods. In detail, the Association Rule Mining (ARM) is applied in order to define the relationships among failure modes and related characteristics that are likely to occur concurrently. The Social Network Analysis (SNA) is then used to represent and analyze these relationships. The main novelty of this work is represented by support in the maintenance management process based not only on the traditional failure analysis but also on a data-driven approach. Moreover, the visual representation of the results provides valuable support in terms of comprehension of the context to implement appropriate actions. The proposed approach is applied to the case study of a hydroelectric power plant, using real-life data.

A Data-Driven Methodology for Fault Detection in Electromechanical Actuators

2014 ◽  
Vol 136 (4) ◽  
Author(s):  
Anthony J. Chirico ◽  
Jason R. Kolodziej
Keyword(s):  
Feature Extraction ◽  
Failure Modes ◽  
Extraction Process ◽  
Data Driven ◽  
Ball Screw ◽  
Fault Classification ◽  
Electromechanical Actuators ◽  
Rotating Machine ◽  
Data Driven Approach ◽  
And Control

This research investigates a novel data-driven approach to condition monitoring of electromechanical actuators (EMAs) consisting of feature extraction and fault classification. The approach is able to accommodate time-varying loads and speeds since EMAs typically operate under nonsteady conditions. The feature extraction process exposes fault frequencies in signal data that are synchronous with motor position through a series of signal processing techniques. A resulting reduced dimension feature is then used to determine the condition with a trained Bayesian classifier. The approach is based on signal analysis in the frequency domain of inherent EMA signals and accelerometers. For this work, two common failure modes, bearing and ball screw faults, are seeded on a MOOG MaxForce EMA. The EMA is then loaded using active and passive load cells with measurements collected via a dSPACE data acquisition and control system. Typical position commands and loads are utilized to simulate “real-world” inputs and disturbances and laboratory results show that actuator condition can be determined over a range of inputs. Although the process is developed for EMAs, it can be used generically on other rotating machine applications as a Health and Usage Management System (HUMS) tool.

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