scholarly journals A Data-Driven Approach for the Diagnosis of Mechanical Systems Using Trained Subtracted Signal Spectrograms

Sensors ◽  
2019 ◽  
Vol 19 (5) ◽  
pp. 1055 ◽  
Author(s):  
Jiung Huh ◽  
Huan Pham Van ◽  
Soonyoung Han ◽  
Hae-Jin Choi ◽  
Seung-Kyum Choi
Keyword(s):  
Frequency Conversion ◽  
Time Synchronization ◽  
Expert Knowledge ◽  
Health Management ◽  
Industrial Robot ◽  
Mechanical Systems ◽  
Data Driven ◽  
Time Frequency ◽  
Data Driven Approach ◽  
The Difference

Toward the prognostic and health management of mechanical systems, we propose and validate a novel effective, data-driven fault diagnosis method. In this method, we develop a trained subtracted spectrogram, the so called critical information map (CIM), identifying the difference between the signal spectrograms of normal and abnormal status. We believe this diagnosis process may be implemented in an autonomous manner so that an engineer employs it without expert knowledge in signal processing or mechanical analyses. Firstly, the CIM method applies sequential and autonomous procedures of time-synchronization, time frequency conversion, and spectral subtraction on raw signal. Secondly, the subtracted spectrogram is then trained to be a CIM for a specific mechanical system failure by finding out the optimal parameters and abstracted information of the spectrogram. Finally, the status of a system health can be monitored accurately by comparing the CIM with an acquired signal map in an automated and timely manner. The effectiveness of the proposed method is successfully validated by employing a diagnosis problem of six-degree-of-freedom industrial robot, which is the diagnosis of a non-stationary system with a small amount of training datasets.

Empirical Mode Decomposition and its Extensions Applied to EEG Analysis: A Review

2018 ◽  
Vol 10 (02) ◽  
pp. 1840001 ◽  
Author(s):  
Catherine M. Sweeney-Reed ◽  
Slawomir J. Nasuto ◽  
Marcus F. Vieira ◽  
Adriano O. Andrade
Keyword(s):  
Empirical Mode Decomposition ◽  
Data Driven ◽  
Eeg Analysis ◽  
Time Frequency ◽  
Computer Interfaces ◽  
Mode Decomposition ◽  
Eeg Data ◽  
Wide Range ◽  
Challenges And Opportunities ◽  
Data Driven Approach

Empirical mode decomposition (EMD) provides an adaptive, data-driven approach to time–frequency analysis, yielding components from which local amplitude, phase, and frequency content can be derived. Since its initial introduction to electroencephalographic (EEG) data analysis, EMD has been extended to enable phase synchrony analysis and multivariate data processing. EMD has been integrated into a wide range of applications, with emphasis on denoising and classification. We review the methodological developments, providing an overview of the diverse implementations, ranging from artifact removal to seizure detection and brain–computer interfaces. Finally, we discuss limitations, challenges, and opportunities associated with EMD for EEG analysis.

scholarly journals Combining Design Patterns and Topic Modeling to Discover Regions That Support Particular Functionality

2019 ◽  
Vol 8 (9) ◽  
pp. 385 ◽  
Author(s):  
Emmanuel Papadakis ◽  
Song Gao ◽  
George Baryannis
Keyword(s):  
Los Angeles ◽  
Topic Modeling ◽  
Design Patterns ◽  
Latent Dirichlet Allocation ◽  
Expert Knowledge ◽  
Urban Setting ◽  
Data Driven ◽  
Knowledge Based ◽  
Functional Regions ◽  
Data Driven Approach

The problem of discovering regions that support particular functionalities in an urban setting has been approached in literature using two general methodologies: top-down, encoding expert knowledge on urban planning and design and discovering regions that conform to that knowledge; and bottom-up, using data to train machine learning models, which can discover similar regions. Both methodologies face limitations, with knowledge-based approaches being criticized for scalability and transferability issues and data-driven approaches for lacking interpretability and depending heavily on data quality. To mitigate these disadvantages, we propose a novel framework that fuses a knowledge-based approach using design patterns and a data-driven approach using latent Dirichlet allocation (LDA) topic modeling in three different ways: Functional regions discovered using either approach are evaluated against each other to identify cases of significant agreement or disagreement; knowledge from patterns is used to adjust topic probabilities in the learning model; and topic probabilities are used to adjust pattern-based results. The proposed methodologies are demonstrated through the use case of identifying shopping-related regions in the Los Angeles metropolitan area. Results show that the combination of pattern-based discovery and topic modeling extraction helps uncover discrepancies between the two approaches and smooth inaccuracies caused by the limitations of each approach.

Data-driven process planning for shipbuilding

2017 ◽  
Vol 32 (1) ◽  
pp. 122-130 ◽  
Author(s):  
Jinsong Bao ◽  
Xiaohu Zheng ◽  
Jianguo Zhang ◽  
Xia Ji ◽  
Jie Zhang
Keyword(s):  
Health Management ◽  
Model Identification ◽  
Production Performance ◽  
Data Driven ◽  
Complex Process ◽  
Data Driven Approach ◽  
Plan Change ◽  
Major Factors ◽  
Relevant Factors

AbstractErection planning in shipbuilding is a highly complex process. When a process change happens for some reason, it is often difficult to identify how many factors are affected and estimate how sensitive these factors can be. To optimize the planning and replanning of the shipbuilding plan for the best production performance, a data-driven approach for shipbuilding erection planning is proposed, which is composed of an erection plan model, identification of major factors related to the erection plan, and a data-driven algorithm to apply shipbuilding operation data for creating plans and forecasting, for plan adjustment, future availabilities of shipyard resources including machines, equipment, and man power. Through data clustering, the relevant factors are identified as a result of plan change, and critical equipment health management is carried out through data-driven anomaly detection. A case study is implemented, and the result shows that the proposed data-driven method is able to reschedule the shipbuilding plans smoothly.

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.

scholarly journals Big Machinery Data Preprocessing Methodology for Data-Driven Models in Prognostics and Health Management

Sensors ◽  
2021 ◽  
Vol 21 (20) ◽  
pp. 6841
Author(s):  
Sergio Cofre-Martel ◽  
Enrique Lopez Droguett ◽  
Mohammad Modarres
Keyword(s):  
Complex Systems ◽  
Expert Knowledge ◽  
Health Management ◽  
Low Cost ◽  
Data Preprocessing ◽  
Data Driven ◽  
Data Sets ◽  
Health State ◽  
Prognostics And Health Management ◽  
Monitoring Networks

Sensor monitoring networks and advances in big data analytics have guided the reliability engineering landscape to a new era of big machinery data. Low-cost sensors, along with the evolution of the internet of things and industry 4.0, have resulted in rich databases that can be analyzed through prognostics and health management (PHM) frameworks. Several data-driven models (DDMs) have been proposed and applied for diagnostics and prognostics purposes in complex systems. However, many of these models are developed using simulated or experimental data sets, and there is still a knowledge gap for applications in real operating systems. Furthermore, little attention has been given to the required data preprocessing steps compared to the training processes of these DDMs. Up to date, research works do not follow a formal and consistent data preprocessing guideline for PHM applications. This paper presents a comprehensive step-by-step pipeline for the preprocessing of monitoring data from complex systems aimed for DDMs. The importance of expert knowledge is discussed in the context of data selection and label generation. Two case studies are presented for validation, with the end goal of creating clean data sets with healthy and unhealthy labels that are then used to train machinery health state classifiers.

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