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.

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 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.

scholarly journals In-Line Monitoring and Control of Rheological Properties through Data-Driven Ultrasound Soft-Sensors

Sensors ◽  
2019 ◽  
Vol 19 (22) ◽  
pp. 5009 ◽  
Author(s):  
Stefania Tronci ◽  
Paul Van Neer ◽  
Erwin Giling ◽  
Uilke Stelwagen ◽  
Daniele Piras ◽  
...  
Keyword(s):  
Performance Indicators ◽  
Continuous Process ◽  
Principal Component ◽  
Feedforward Neural Networks ◽  
Data Driven ◽  
Monitoring And Control ◽  
Soft Sensors ◽  
Continuous Processes ◽  
Data Driven Approach ◽  
And Control

The use of continuous processing is replacing batch modes because of their capabilities to address issues of agility, flexibility, cost, and robustness. Continuous processes can be operated at more extreme conditions, resulting in higher speed and efficiency. The issue when using a continuous process is to maintain the satisfaction of quality indices even in the presence of perturbations. For this reason, it is important to evaluate in-line key performance indicators. Rheology is a critical parameter when dealing with the production of complex fluids obtained by mixing and filling. In this work, a tomographic ultrasonic velocity meter is applied to obtain the rheological curve of a non-Newtonian fluid. Raw ultrasound signals are processed using a data-driven approach based on principal component analysis (PCA) and feedforward neural networks (FNN). The obtained sensor has been associated with a data-driven decision support system for conducting the process.

scholarly journals Data-driven approach for monitoring, protection, and control of distribution system assets using micro-PMU technology

2017 ◽  
Vol 2017 (1) ◽  
pp. 1011-1014 ◽  
Author(s):  
Emma Stewart ◽  
Michael Stadler ◽  
Ciaran Roberts ◽  
Jim Reilly ◽  
Dan Arnold ◽  
...  
Keyword(s):  
Distribution System ◽  
Data Driven ◽  
Data Driven Approach ◽  
Protection And Control ◽  
And Control

A data-driven approach for the health prognosis of high-speed train wheels

2020 ◽  
Vol 234 (6) ◽  
pp. 735-747 ◽  
Author(s):  
Zhexiang Chi ◽  
Taotao Zhou ◽  
Simin Huang ◽  
Yan-Fu Li
Keyword(s):  
High Speed ◽  
Failure Modes ◽  
Sampling Technique ◽  
Bayesian Optimization ◽  
Data Driven ◽  
Support Vector ◽  
High Speed Train ◽  
Mixed Features ◽  
Data Driven Approach ◽  
Polygonal Wear

Polygonal wear is one of the most critical failure modes of high-speed train wheels that would significantly compromise the safety and reliability of high-speed train operation. However, the mechanism underpinning wheel polygon is complex and still not fully understood, which makes it challenging to track its evolution of the polygonal wheel. The large amount of data gathered through regular inspection and maintenance of Chinese high-speed trains provides a promising way to tackle this challenge with data-driven methods. This article proposes a data-driven approach to predict the degree of the polygonal wear, assess the reliability of individual wheels and the health index of all wheels of a high-speed train for maintenance priority ranking. The synthetic minority over-sampling technique—nominal continuous is adopted to augment the maintenance dataset of imbalanced and mixed features. The autoencoder is used to learn abstract features to represent the original datasets, which are then fed into a support vector machine classifier. The approach is coherently optimized by tuning the model hyper-parameters based on Bayesian optimization. The effectiveness of our proposed approach is demonstrated by the wheel maintenance data obtained from the year 2016 to 2017. The results can also be used to support practical maintenance priority allocation.

scholarly journals Risk Assessment of the Overseas Imported COVID-19 of Ocean-Going Ships Based on AIS and Infection Data

2020 ◽  
Vol 9 (6) ◽  
pp. 351 ◽  
Author(s):  
Zhihuan Wang ◽  
Mengyuan Yao ◽  
Chenguang Meng ◽  
Christophe Claramunt
Keyword(s):  
Spatial Clustering ◽  
Infection Risk ◽  
Data Driven ◽  
Automatic Identification ◽  
Quick Response ◽  
Support Mechanism ◽  
Risk Levels ◽  
Data Driven Approach ◽  
Spatio Temporal ◽  
And Control

Preventing and controlling the risk of importing the coronavirus disease (COVID-19) has rapidly become a major concern. In addition to air freight, ocean-going ships play a non-negligible role in spreading COVID-19 due to frequent visits to countries with infected populations. This research introduces a method to dynamically assess the infection risk of ships based on a data-driven approach. It automatically identifies the ports and countries these ships approach based on their Automatic Identification Systems (AIS) data and a spatio-temporal density-based spatial clustering of applications with noise (ST_DBSCAN) algorithm. We derive daily and 14 day cumulative ship exposure indexes based on a series of country-based indices, such as population density, cumulative confirmed cases, and increased rate of confirmed cases. These indexes are classified into high-, middle-, and low-risk levels that are then coded as red, yellow, and green according to the health Quick Response (QR) code based on the reference exposure index of Wuhan on April 8, 2020. This method was applied to a real container ship deployed along a Eurasian route. The results showed that the proposed method can trace ship infection risk and provide a decision support mechanism to prevent and control overseas imported COVID-19 cases from international shipping.

A Benchmarking Analysis of a Data-Driven Gas Turbine Diagnostic Approach

2018 ◽  
Author(s):  
Igor Loboda ◽  
Juan Luis Pérez-Ruiz ◽  
Sergiy Yepifanov
Keyword(s):  
Feature Extraction ◽  
Fault Detection ◽  
Gas Turbine ◽  
Software Tool ◽  
Aircraft Engine ◽  
Data Driven ◽  
Fault Classification ◽  
Fault Identification ◽  
Diagnostic Process ◽  
Promising Tool

In an effort to better compare particular gas turbine diagnostic solutions and recommend the best solution, the software tool called Propulsion Diagnostic Method Evaluation Strategy (ProDiMES) has been developed. This benchmarking platform includes a simulator of the aircraft engine fleet with healthy and faulty engines. The platform presents a public approach, at which different investigators can verify and compare their algorithms for the diagnostic stages of feature extraction, fault detection, and fault identification. Using ProDiMES, some different diagnostic solutions have been compared so far. This study presents a new attempt to enhance a gas turbine diagnostic process. A data-driven algorithm that embraces the mentioned three diagnostic stages is verified on the basis of ProDiMES. At the feature extraction stage, this algorithm uses a polynomial model of an engine baseline to compute deviations of actual gas path measurements from the corresponding values of a healthy engine. At the fault detection and fault identification stages, a common classification for fault detection and fault identification is firstly constructed using deviation vectors (patterns). One of the three chosen pattern recognition techniques then performs both fault detection and fault identification as a common process. Numerous numerical experiments have been conducted to select the best configurations of the baseline model, a pertinent structure of the fault classification, and the best recognition technique. The experiments were accompanied by a computational precision analysis for each component of the proposed algorithm. The comparison of the final diagnostic ProDiMES metrics obtained under the selected optimal conditions with the metrics of other diagnostic solutions shows that the proposed algorithm is a promising tool for gas turbine monitoring systems.

Sign in / Sign up

Export Citation Format

Share Document