A Layering Linear Discriminant Analysis-Based Fault Diagnosis Method for Grid-Connected Inverter

Abstract

Grid-connected inverters are the core equipment for connecting marine energy power generation systems to the public electric utility. The variation of current sensor fault severity will make fault samples multimodal. However, linear discriminant analysis assumes that the same fault is independent and identically distributed. To solve this problem, this paper proposes a layering linear discriminant analysis method based on traditional linear discriminant analysis. The proposed method divides the historical fault data based on the sensor fault severity layer-by-layer until the distribution of the same fault category in each subset is very close. Linear discriminant analysis is used to analyze historical fault data in each subgroup, and the kappa coefficient is applied as the basis for ending the training process. A BP neural network is employed to estimate the fault severity during the testing process, and the fault diagnosis sub-model is selected. The proposed method enables the accurate diagnosis of faults with different distributions in the same category and provides an accurate estimate of the sensor’s fault severity degree. The estimated value of the sensor’s fault degree can provide critical information for the maintenance of the equipment and can be used to correct the sensor’s output.

A Fault-Tolerant Control Method Based on Reconfiguration SPWM Signal for Cascaded Multilevel IGBT-Based Propulsion in Electric Ships

“…For fault diagnosis in a ship's DC electrical system, there has been a lot of research developments, such as a convolutional-neural-network-based method [21], Res-BiLSTM [22], and a layering linear discriminant analysis [23].…”

“…Fault diagnosis is an essential step that is responsible for determining fault information and activating the corresponding fault-tolerant control method. Some effective diagnosis methods have been proposed in the field of ships [21][22][23]. This paper mainly focuses on the research on fault-tolerant control of open-circuit faults.…”

Enhancement of Recommendation Engine Technique for Bug System Fixes

“…The statistical method known as Term Frequency-Inverse Document Frequency (TF-IDF, which is a metric utilized in machine learning and information retrieval) is employed to observe the syntactic variation of terms in documents. Jin et al [9] employ an enhanced Linear Discriminant Analysis model to automate the classification of faults. The eligible bug reports were defined based on the distribution of bug problems, and the similarities between the bug fix creator and the distribution were examined.…”

Incipient Fault Diagnosis of a Grid-Connected T-Type Multilevel Inverter Using Multilayer Perceptron and Walsh Transform

“…The consistency between the observed behavior of the operating system and the knowledge base is then checked, leading to a fault diagnosis decision with the aid of a classifier. The most well-known quantitative knowledge-based fault diagnosis methods are analytical models, neural networks (NNs), principal component analysis (PCA), partial least squares (PLS), independent component analysis (ICA), statistical pattern classifiers, and support vector machine (SVM) [45][46][47][48][49][50][51][52][53]. The most common qualitative process models are qualitative trend analysis (QTA) models, signed direct graph (SDG) models, and fuzzy logic models [54][55][56][57][58][59].…”