scholarly journals A SOM based Anomaly Detection Method for Wind Turbines Health Management through SCADA Data

2020 ◽  
Vol 7 (4) ◽  
Author(s):  
Mian Du ◽  
Lina Bertling Tjernberg ◽  
Shicong Ma ◽  
Qing He ◽  
Lin Cheng ◽  
...  
Keyword(s):  
Anomaly Detection ◽  
Health Management ◽  
Quantile Function ◽  
System Level ◽  
Self Organizing Map ◽  
Measurement Interval ◽  
Physical Knowledge ◽  
Failure Data ◽  
Wind Turbine System ◽  
Component Identification

In this paper, a data driven method for Wind Turbine system level anomaly detection and root sub-component identification is proposed. Supervisory control and data acquisition system (SCADA) data of WT is adopted and several parameters are selected based on physical knowledge in this domain and correlation coefficient analysis to build a normal behavior model. This model which is based on Self-organizing map (SOM) projects higher-dimensional SCADA data into a two-dimension-map. Afterwards, the Euclidean distance based indicator for system level anomalies is defined and a filter is created to screen out suspicious data points based on quantile function. Moreover, a failure data pattern based criterion is created for anomaly detection from system level. In order to track which sub-component should be responsible for an anomaly, a contribution proportion (CP) index is proposed. The method is tested with a two-month SCADA dataset with the measurement interval as 20 seconds. Results demonstrate capability and efficiency of the proposed method.

scholarly journals Deep Learning with Dynamically Weighted Loss Function for Sensor-Based Prognostics and Health Management

Sensors ◽  
2020 ◽  
Vol 20 (3) ◽  
pp. 723 ◽  
Author(s):  
Divish Rengasamy ◽  
Mina Jafari ◽  
Benjamin Rothwell ◽  
Xin Chen ◽  
Grazziela P. Figueredo
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Loss Function ◽  
Short Term Memory ◽  
Health Management ◽  
Remaining Useful Life ◽  
Loss Functions ◽  
Pressure System ◽  
Failure Data ◽  
Prognostic And Health Management

Deep learning has been employed to prognostic and health management of automotive and aerospace with promising results. Literature in this area has revealed that most contributions regarding deep learning is largely focused on the model’s architecture. However, contributions regarding improvement of different aspects in deep learning, such as custom loss function for prognostic and health management are scarce. There is therefore an opportunity to improve upon the effectiveness of deep learning for the system’s prognostics and diagnostics without modifying the models’ architecture. To address this gap, the use of two different dynamically weighted loss functions, a newly proposed weighting mechanism and a focal loss function for prognostics and diagnostics task are investigated. A dynamically weighted loss function is expected to modify the learning process by augmenting the loss function with a weight value corresponding to the learning error of each data instance. The objective is to force deep learning models to focus on those instances where larger learning errors occur in order to improve their performance. The two loss functions used are evaluated using four popular deep learning architectures, namely, deep feedforward neural network, one-dimensional convolutional neural network, bidirectional gated recurrent unit and bidirectional long short-term memory on the commercial modular aero-propulsion system simulation data from NASA and air pressure system failure data for Scania trucks. Experimental results show that dynamically-weighted loss functions helps us achieve significant improvement for remaining useful life prediction and fault detection rate over non-weighted loss function predictions.

SYSTEM RELIABILITY GROWTH ANALYSIS USING COMPONENT FAILURE DATA

1994 ◽  
Vol 01 (01) ◽  
pp. 71-83 ◽  
Author(s):  
M. XIE ◽  
T.N. GOH
Keyword(s):  
System Reliability ◽  
Growth Analysis ◽  
Simple Approach ◽  
System Level ◽  
Reliability Growth ◽  
System Failure ◽  
Component Level ◽  
Component Failure ◽  
Failure Data ◽  
Growth Estimation

In this paper the problem of system-level reliability growth estimation using component-level failure data is studied. It is suggested that system failure data should be broken down into component, or subsystem, failure data when the above problems have occurred during the system testing phase. The proposed approach is especially useful when the system is not unchanged over the time, when some subsystems are improved more than others, or when the testing has been concentrated on different components at different time. These situations usually happen in practice and it may also be the case even if the system failure data is provided. Two sets of data are used to illustrate the simple approach; one is a set of component failure data for which all subsystems are available for testing at the same time and for the other set of data, the starting times are different for different subsystems.

Electromechanical Actuator Test Stand Coupling Design to Support Actuator Prognostic Model Development

2011 ◽  
Author(s):  
Michael T. Koopmans ◽  
Irem Y. Tumer
Keyword(s):  
Prognostic Model ◽  
Model Development ◽  
Uncertainty Assessment ◽  
Test Stand ◽  
System Level ◽  
Data Sets ◽  
Component Level ◽  
Electromechanical Actuator ◽  
Response Data ◽  
Failure Data

Uncertainty assessment and management is becoming an increasingly essential aspect of good prognostic design for engineering complex systems. Uncertainty surrounding diagnostics, loads, and fault progression models is very real and propagating this uncertainty from component-level health estimates to the system-level remains difficult at best. In this work, a test stand is used to conduct real-time failure experiments aboard various aircraft platforms to collect failure response data, expanding the actuator knowledge base that forms the foundation of component health estimations. The research takes a step towards standardizing a test stand design to produce comparable and scalable failure data sets, fostering uncertainty reduction within the electromechanical actuator prognostic model. This paper specifically presents a method to optimize the actuator coupling for a commercially available actuator where a model was built to minimize the coupling deflection and estimate the coupling life. Using this model, researchers can rapidly develop their own electromechanical actuator test stands.

A Multidisciplinary and Multiobjective System Analysis and Optimization Methodology for Embedding Integrated Systems Health Management (ISHM) Into NASA’s Complex Systems

2006 ◽  
Author(s):  
Ali Farhang Mehr ◽  
Irem Y. Tumer
Keyword(s):  
System Analysis ◽  
Health Management ◽  
Space Mission ◽  
Shared Values ◽  
System Level ◽  
Integrated Systems ◽  
Subsystem Level ◽  
Optimization Methodology ◽  
Design Protocol ◽  
Component Failures

NASA’s future space exploration systems will include a highly complex Integrated Systems Health Management (ISHM) capability, which can detect, predict, isolate and respond to system and component failures in order to improve safety and maintainability. An ISHM system, as a whole, consists of several subsystems that monitor different components of a space mission. Due to the complex and multidisciplinary nature of designing ISHM, there seems to be a lack of formal methodologies to design an optimal (or near-optimal) ISHM for a given system of systems. In this research, we propose a new methodology to design and optimize ISHM as a distributed system with multiple interacting disciplines as well as multiple conflicting design objectives (i.e. Figures Of Merit or FOMs). This specialized multidisciplinary design approach can be used to optimize the effectiveness of ISHM systems for future NASA missions. We assume a hierarchical design protocol, where each subsystem communicates with other subsystems only in a top-down tree structure. At the top level, the overall performance of the mission consists of system-level variables, parameters, objectives, and constraints that are shared throughout the system and by all subsystems. Each subsystem will then comprise of these shared values in addition to those values that are specific to subsystems. As a specific case study, we take the example of designing an ISHM capability for X-34 reusable launch vehicle in two levels. The proposed approach, referred to as ISHM Multidisciplinary and Multiobjective System Analysis & Optimization (or ISHM MMSA&O), has a hierarchical structure to pass up or down shared values between the two levels with system-level and subsystem-level optimization routines.

Machine Anomaly Detection and Diagnosis Incorporating Operational Data Applied to Feed Axis Health Monitoring

2011 ◽  
Author(s):  
Linxia Liao ◽  
Radu Pavel
Keyword(s):  
Anomaly Detection ◽  
Principal Component ◽  
Operating Conditions ◽  
Self Organizing Map ◽  
Test Bed ◽  
Accurate Analysis ◽  
Operating Condition ◽  
Detection And Diagnosis ◽  
Feed Axis ◽  
Operational Data

Solutions for machinery anomaly detection and diagnosis are typically designed on an ad hoc, custom basis, and previous studies have shown limited success in automating or generalizing these solutions. Reusing and maintaining the analysis software, especially when the machine usage pattern or operating condition changes, remains a challenge. This paper outlines a strategy to make use of operational data obtained from the machine’s controller and signals obtained from external sensors to provide an accurate analysis within each operating condition. Operational data collected from the controller is used both for labeling datasets into different operating conditions and for analysis. Principal component analysis (PCA) is adopted to identify critical sensors that can provide useful information. Self-organizing map (SOM)-based anomaly detection and diagnosis methods are used to automatically convert data to easily understandable machine health information for operators. Experiment trials conducted on a feed-axis test-bed demonstrated the effectiveness of incorporating operational data for anomaly detection and diagnosis.

scholarly journals A Parameter Selection Method for Wind Turbine Health Management through SCADA Data

2017 ◽  
Author(s):  
Mian Du ◽  
Jun Yi ◽  
Peyman Mazidi ◽  
Lin Cheng ◽  
Jianbo Guo
Keyword(s):  
Probability Distribution ◽  
Mutual Information ◽  
Wind Turbine ◽  
Health Management ◽  
Estimation Method ◽  
Failure Detection ◽  
Health Condition ◽  
Parameter Selection ◽  
Machine Learning Techniques ◽  
Physical Knowledge

More and more works are using machine learning techniques while adopting supervisory control and data acquisition (SCADA) system for wind turbine anomaly or failure detection. While parameter selection is important for modelling a wind turbine’s health condition, only a few papers have been published focusing on this issue and in those papers interconnections among sub-components in a wind turbine are used to address this problem. However, merely the interconnections for decision making sometimes is too general to provide a parameter list considering the differences of each SCADA dataset. In this paper, a method is proposed to provide more detailed suggestions on parameter selection based on mutual information. Moreover, after proving that Copula, a multivariate probability distribution for which the marginal probability distribution of each variable is uniform is capable of simplifying the estimation of mutual information, an empirical copula based mutual information estimation method (ECMI) is introduced for an application. After that, a real SCADA dataset is adopted to test the method, and the results show the effectiveness of the ECMI in providing parameter selection suggestions when physical knowledge is not accurate enough.

scholarly journals Challenges and Opportunities of System-Level Prognostics

Sensors ◽  
2021 ◽  
Vol 21 (22) ◽  
pp. 7655
Author(s):  
Seokgoo Kim ◽  
Joo-Ho Choi ◽  
Nam H. Kim
Keyword(s):  
Health Management ◽  
System Level ◽  
Prognostics And Health Management ◽  
Health Index ◽  
System Operation ◽  
Component Level ◽  
Industrial Systems ◽  
Multiple Components ◽  
Challenges And Opportunities ◽  
Essential Function

Prognostics and health management (PHM) has become an essential function for safe system operation and scheduling economic maintenance. To date, there has been much research and publications on component-level prognostics. In practice, however, most industrial systems consist of multiple components that are interlinked. This paper aims to provide a review of approaches for system-level prognostics. To achieve this goal, the approaches are grouped into four categories: health index-based, component RUL-based, influenced component-based, and multiple failure mode-based prognostics. Issues of each approach are presented in terms of the target systems and employed algorithms. Two examples of PHM datasets are used to demonstrate how the system-level prognostics should be conducted. Challenges for practical system-level prognostics are also addressed.

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