Explainable Incipient Fault Detection Systems for Photovoltaic Panels
- URL: http://arxiv.org/abs/2011.09843v1
- Date: Thu, 19 Nov 2020 14:26:29 GMT
- Title: Explainable Incipient Fault Detection Systems for Photovoltaic Panels
- Authors: S. Sairam, Seshadhri Srinivasan, G. Marafioti, B. Subathra, G.
Mathisen, and Korkut Bekiroglu
- Abstract summary: The XFDDS is a hybrid approach that combines the model-based and data-driven framework.
Model-based FDD for PV panels lacks high fidelity models at low irradiance conditions for detecting incipient faults.
Lack of explainability, feature variability for sample instances, and false alarms are challenges with data-driven FDD methods.
- Score: 0.057725463942541105
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: This paper presents an eXplainable Fault Detection and Diagnosis System
(XFDDS) for incipient faults in PV panels. The XFDDS is a hybrid approach that
combines the model-based and data-driven framework. Model-based FDD for PV
panels lacks high fidelity models at low irradiance conditions for detecting
incipient faults. To overcome this, a novel irradiance based three diode model
(IB3DM) is proposed. It is a nine parameter model that provides higher accuracy
even at low irradiance conditions, an important aspect for detecting incipient
faults from noise. To exploit PV data, extreme gradient boosting (XGBoost) is
used due to its ability to detecting incipient faults. Lack of explainability,
feature variability for sample instances, and false alarms are challenges with
data-driven FDD methods. These shortcomings are overcome by hybridization of
XGBoost and IB3DM, and using eXplainable Artificial Intelligence (XAI)
techniques. To combine the XGBoost and IB3DM, a fault-signature metric is
proposed that helps reducing false alarms and also trigger an explanation on
detecting incipient faults. To provide explainability, an eXplainable
Artificial Intelligence (XAI) application is developed. It uses the local
interpretable model-agnostic explanations (LIME) framework and provides
explanations on classifier outputs for data instances. These explanations help
field engineers/technicians for performing troubleshooting and maintenance
operations. The proposed XFDDS is illustrated using experiments on different PV
technologies and our results demonstrate the perceived benefits.
Related papers
- Fault Detection and Monitoring using an Information-Driven Strategy: Method, Theory, and Application [5.056456697289351]
We propose an information-driven fault detection method based on a novel concept drift detector.
The method is tailored to identifying drifts in input-output relationships of additive noise models.
We prove several theoretical properties of the proposed MI-based fault detection scheme.
arXiv Detail & Related papers (2024-05-06T17:43:39Z) - DetDiffusion: Synergizing Generative and Perceptive Models for Enhanced Data Generation and Perception [78.26734070960886]
Current perceptive models heavily depend on resource-intensive datasets.
We introduce perception-aware loss (P.A. loss) through segmentation, improving both quality and controllability.
Our method customizes data augmentation by extracting and utilizing perception-aware attribute (P.A. Attr) during generation.
arXiv Detail & Related papers (2024-03-20T04:58:03Z) - Diffusion-Based Particle-DETR for BEV Perception [94.88305708174796]
Bird-Eye-View (BEV) is one of the most widely-used scene representations for visual perception in Autonomous Vehicles (AVs)
Recent diffusion-based methods offer a promising approach to uncertainty modeling for visual perception but fail to effectively detect small objects in the large coverage of the BEV.
Here, we address this problem by combining the diffusion paradigm with current state-of-the-art 3D object detectors in BEV.
arXiv Detail & Related papers (2023-12-18T09:52:14Z) - An Improved Anomaly Detection Model for Automated Inspection of Power Line Insulators [0.0]
Inspection of insulators is important to ensure reliable operation of the power system.
Deep learning is being increasingly exploited to automate the inspection process.
This article proposes the use of anomaly detection along with object detection in a two-stage approach for incipient fault detection.
arXiv Detail & Related papers (2023-11-14T11:36:20Z) - Causal Disentanglement Hidden Markov Model for Fault Diagnosis [55.90917958154425]
We propose a Causal Disentanglement Hidden Markov model (CDHM) to learn the causality in the bearing fault mechanism.
Specifically, we make full use of the time-series data and progressively disentangle the vibration signal into fault-relevant and fault-irrelevant factors.
To expand the scope of the application, we adopt unsupervised domain adaptation to transfer the learned disentangled representations to other working environments.
arXiv Detail & Related papers (2023-08-06T05:58:45Z) - Non-contact Sensing for Anomaly Detection in Wind Turbine Blades: A
focus-SVDD with Complex-Valued Auto-Encoder Approach [2.967390112155113]
We enhance the quality assurance of manufacturing utilizing FMCW radar as a non-destructive sensing modality.
We propose a novel anomaly detection methodology called focus Support Vector Data Description (focus-SVDD)
The effectiveness of the proposed method is demonstrated through its application to collected data.
arXiv Detail & Related papers (2023-06-19T09:54:34Z) - Machine Learning for UAV Propeller Fault Detection based on a Hybrid
Data Generation Model [0.0]
This paper focuses on the identification of faulty propellers and classification of the fault level for quadrotor UAVs using RPM as well as flight data.
To achieve offline training data generation, a hybrid approach is proposed for the development of a virtual data-generative model.
The experimental results obtained show that our trained model can identify the location of propeller fault as well as the degree/type of damage.
arXiv Detail & Related papers (2023-02-03T05:28:02Z) - Fault Detection and Diagnosis with Imbalanced and Noisy Data: A Hybrid
Framework for Rotating Machinery [2.580765958706854]
Fault diagnosis plays an essential role in reducing the maintenance costs of rotating machinery manufacturing systems.
Traditional Fault Detection and Diagnosis (FDD) frameworks get poor performances when dealing with real-world circumstances.
This paper proposes a hybrid framework which uses the three aforementioned components to achieve an effective signal-based FDD system.
arXiv Detail & Related papers (2022-02-09T01:09:59Z) - On-board Fault Diagnosis of a Laboratory Mini SR-30 Gas Turbine Engine [54.650189434544146]
A data-driven fault diagnosis and isolation scheme is explicitly developed for failure in the fuel supply system and sensor measurements.
A model is trained using machine learning classifiers to detect a given set of fault scenarios in real-time on which it is trained.
Several simulation studies were carried out to demonstrate and illustrate the proposed fault diagnosis scheme's advantages, capabilities, and performance.
arXiv Detail & Related papers (2021-10-17T13:42:37Z) - DAE : Discriminatory Auto-Encoder for multivariate time-series anomaly
detection in air transportation [68.8204255655161]
We propose a novel anomaly detection model called Discriminatory Auto-Encoder (DAE)
It uses the baseline of a regular LSTM-based auto-encoder but with several decoders, each getting data of a specific flight phase.
Results show that the DAE achieves better results in both accuracy and speed of detection.
arXiv Detail & Related papers (2021-09-08T14:07:55Z) - SUOD: Accelerating Large-Scale Unsupervised Heterogeneous Outlier
Detection [63.253850875265115]
Outlier detection (OD) is a key machine learning (ML) task for identifying abnormal objects from general samples.
We propose a modular acceleration system, called SUOD, to address it.
arXiv Detail & Related papers (2020-03-11T00:22:50Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.