Fuzzy Pattern Recognition Based Fault Diagnosis

International audienceIn order to avoid catastrophic situations when the dynamics of a physical system (entity in Multi Agent System architecture) are evolving toward an undesirable operating mode, particular and quick safety actions have to be programmed in the control design. Classic control (PID and even state model based methods) becomes powerless for complex plants (nonlinear, MIMO and ill-defined systems). A more efficient diagnosis requires an artificial intelligence approach. We propose in this paper the design of a Fuzzy Pattern Recognition System (FPRS) that solves, in real time, the main following problems: 1) Identification of an actual state; 2) Identification of an eventual evolution towards a failure state; 3) Diagnosis and decision-making. Simulations have been carried for a fictive complex process plant with the objective to evaluate the consistency and the performance of the proposed diagnosis philosophy. The obtained results seem to be encouraging and very promising for application to fault diagnosis of a real and complex plant process

Modeling and Evaluation of Single Machine Flexibility Using Fuzzy Entropy and Genetic Algorithm Based Approach

International audienceFlexibility has long been recognized as a manufacturing capability that has the potential to impact mainly the competitive position of an organization. The entropy approach, which was extended from information theory, fell in handling problems with incomplete and uncertain data, because it depicts only the stochastic aspects included with measured observations. In order to get a global view, this work proposes a new approach based on fuzzy entropy concept. The development of the fuzzy model results in a set of nonlinear constrained problems to be solved using a metaheuristics method. The applicability of our approach is illustrated through a flexible manufacturing cell. By adopting such framework, both dimensions of uncertainty in system modeling, expressed by stochastic variability and imprecision, can be taken into consideration

A Smart Algorithm for the Diagnosis of Short-Circuit Faults in a Photovoltaic Generator

International audienceThis paper deals with a smart algorithm allowing short-circuit faults detection and diagnosis of PV generators. The proposed algorithm is based on the hybridization of a support vector machines (SVM) technique optimized by a k-NN tool for the classification of observations on the classifier itself or located in its margin. To test the proposed algorithm performance, a PV generator database containing observations distributed over classes is used for simulation purposes

A Regression Algorithm for the Smart Prognosis of a Reversed Polarity Fault in a Photovoltaic Generator

International audienceThis paper deals with a smart algorithm allowing reversed polarity fault diagnosis and prognosis in PV generators. The proposed prognosis (prediction) approach is based on the hybridization of a support vector regression (SVR) technique optimized by a k-NN regression tool (K-NNR) for undetermined outputs. To test the proposed algorithm performance, a PV generator database containing sample data is used for simulation purposes

Electrical Faults Modeling of the Photovoltaic Generator

International audienceIn this paper, we presented a new methodology for the mathematical modeling of the photovoltaic generator's characteristics based on known electrical laws. This proposed new methodology in this work consists of a three new algorithms, each one presents the characteristic of the cell, group of cells, module, string and generator, when one or more of its components : cells, bypass diodes and blocking diodes subjected to these types of defaults: reversed polarity, open circuit, short circuit or impedance. The three new algorithms obtained can facilitate the prediction for the prognosis or the detection for the diagnosis of these photovoltaic generator's defaults

Machine Learning for Photovoltaic Systems Condition Monitoring:A Review

Condition Monitoring of photovoltaic systems plays an important role in maintenance interventions due to its ability to solve problems of loss of energy production revenue. Nowadays, machine learning-based failure diagnosis is becoming increasingly growing as an alternative to various difficult physical-based interpretations and the main pile foundation for condition monitoring. As a result, several methods with different learning paradigms (e.g. deep learning, transfer learning, reinforcement learning, ensemble learning, etc.) have been used to address different condition monitoring issues. Therefore, the aim of this paper is at least, to shed light on the most relevant work that has been done so far in the field of photovoltaic systems machine learning-based condition monitoring

Machine Learning-Based Condition Monitoring for PV Systems: State of the Art and Future Prospects

From MDPI via Jisc Publications RouterHistory: accepted 2021-09-30, pub-electronic 2021-10-03Publication status: PublishedTo ensure the continuity of electric power generation for photovoltaic systems, condition monitoring frameworks are subject to major enhancements. The continuous uniform delivery of electric power depends entirely on a well-designed condition maintenance program. A just-in-time task to deal with several naturally occurring faults can be correctly undertaken via the cooperation of effective detection, diagnosis, and prognostic analyses. Therefore, the present review first outlines different failure modes to which all photovoltaic systems are subjected, in addition to the essential integrated detection methods and technologies. Then, data-driven paradigms, and their contribution to solving this prediction problem, are also explored. Accordingly, this review primarily investigates the different learning architectures used (i.e., ordinary, hybrid, and ensemble) in relation to their learning frameworks (i.e., traditional and deep learning). It also discusses the extension of machine learning to knowledge-driven approaches, including generative models such as adversarial networks and transfer learning. Finally, this review provides insights into different works to highlight various operating conditions and different numbers and types of failures, and provides links to some publicly available datasets in the field. The clear organization of the abundant information on this subject may result in rigorous guidelines for the trends adopted in the future

Leveraging Label Information in a Knowledge-Driven Approach for Rolling-Element Bearings Remaining Useful Life Prediction

Since bearing deterioration patterns are difficult to collect from real, long lifetime scenarios, data-driven research has been directed towards recovering them by imposing accelerated life tests. Consequently, insufficiently recovered features due to rapid damage propagation seem more likely to lead to poorly generalized learning machines. Knowledge-driven learning comes as a solution by providing prior assumptions from transfer learning. Likewise, the absence of true labels was able to create inconsistency related problems between samples, and teacher-given label behaviors led to more ill-posed predictors. Therefore, in an attempt to overcome the incomplete, unlabeled data drawbacks, a new autoencoder has been designed as an additional source that could correlate inputs and labels by exploiting label information in a completely unsupervised learning scheme. Additionally, its stacked denoising version seems to more robustly be able to recover them for new unseen data. Due to the non-stationary and sequentially driven nature of samples, recovered representations have been fed into a transfer learning, convolutional, long–short-term memory neural network for further meaningful learning representations. The assessment procedures were benchmarked against recent methods under different training datasets. The obtained results led to more efficiency confirming the strength of the new learning path

ProgNet: A Transferable Deep Network for Aircraft Engine Damage Propagation Prognosis under Real Flight Conditions

Machine learning prognosis for condition monitoring of safety-critical systems, such as aircraft engines, continually faces challenges of data unavailability, complexity, and drift. Consequently, this paper overcomes these challenges by introducing adaptive deep transfer learning methodologies, strengthened with robust feature engineering. Initially, data engineering encompassing: (i) principal component analysis (PCA) dimensionality reduction; (ii) feature selection using correlation analysis; (iii) denoising with empirical Bayesian Cauchy prior wavelets; and (iv) feature scaling is used to obtain the required learning representations. Next, an adaptive deep learning model, namely ProgNet, is trained on a source domain with sufficient degradation trajectories generated from PrognosEase, a run-to-fail data generator for health deterioration analysis. Then, ProgNet is transferred to the target domain of obtained degradation features for fine-tuning. The primary goal is to achieve a higher-level generalization while reducing algorithmic complexity, making experiments reproducible on available commercial computers with quad-core microprocessors. ProgNet is tested on the popular New Commercial Modular Aero-Propulsion System Simulation (N-CMAPSS) dataset describing real flight scenarios. To the extent we can report, this is the first time that all N-CMAPSS subsets have been fully screened in such an experiment. ProgNet evaluations with numerous metrics, including the well-known CMAPSS scoring function, demonstrate promising performance levels, reaching 234.61 for the entire test set. This is approximately four times better than the results obtained with the compared conventional deep learning models

Optimization of SVM Classifier by k-NN for the Smart Diagnosis of the Short-Circuit and Impedance Faults in a PV Generator

International audienceThis paper deals with a new algorithm allowing short-circuit and impedance faults smart diagnosis of PV generators. It is based on the use of the SVM technique for the classification of observations not located in its margin, otherwise the proposed algorithm is used a k-NN method. A PV generator database containing observations distributed over classes is used for testing the new algorithm performance, which shows therefore its contribution and its effectiveness in the diagnosis area. * Parameter j of new observation x *. I' Identity matrix. J Tuning parameter for error accepted. I Current. V Voltage. P Power. PH Photocurrent. I/V Cell Current / Voltage of PV cell. I/V Group Current / Voltage of PV group. I/V Module Current / Voltage of PV module. I/V String Current / Voltage of PV string. I Bypass_Diode Bypass diode current. R s series resistance. t Temperature