A Fault Detection Method Based on the Dynamic k-Nearest Neighbor Model and Dual Control Chart

In chemical processes, there are both nonlinearity and slow dynamics among process variables. Meanwhile, at the beginning of faults, slight variation may not prompt the significant parametric changes. Traditional fault detection methods, based on mean and variation shifts, often fail to achieve satisfactory performance. To improve the detection of incipient quality-related faults, a novel orthonormal slow feature-based quality-related data decomposition network with Jensen-Shannon divergence (OSF-JS) fault detection method is proposed. The orthonormal subspace analysis (OSA) is embedded with Siamese to extract orthonormal slow features (OSFs) from decomposed subspaces. Enhancing the detection of incipient faults, JS divergence combined with a sliding window algorithm is introduced to construct statistics. The effectiveness of OSF-JS is validated for a wastewater treatment process and a real-world chemical process. Results show that the proposed OSF-JS method not only improves the fault detection rate (FDR) and reduces false alarm rate (FAR) but also identifies whether faults occur in the quality-related component.

Most of the traditional data-driven fault detection methods depend on the change of mean value and the shift of variables. However, incipient faults are characterized by low amplitude and masked by unknown disturbances and noise. Incipient faults are not detected effectively by the traditional data-driven methods. If incipient faults are ignored, it will cause potential problems to the operation of industrial processes. It is of great importance to study the detection of incipient faults. In this study, a Kolmogorov-Smironv (K-S) test based incipient fault detection method is proposed. First, the sample data is divided into training data and test data; second, a principal component analysis (PCA) model is established with the training data to obtain the loading matrix; third, the principal component subspace (PCS) of the training data and the PCS of the test data are calculated with the load matrix; fourth, the empirical distribution function (EDF) of the principal components of the training data and the test data are calculated in each sliding window; finally, K-S statistics are calculated to determine whether a fault occurs. Moreover, the effectiveness of the proposed solution is verified in the continuous stirred tank heater (CSTH) process simulation models.

Although doubly fed induction generators (DFIG) are widely used, difficulties in early fault detection and severity assessment for inter-turn short-circuit (ITSC) faults are highly prominent. In this manuscript, a novel incipient fault detection and state assessment method based on external leakage flux sensing and modified multiscale poincare plots (MMSPP) is proposed. An external leakage flux sensor is placed on the axial-end position of the generator to monitor the presence and evolution of ITSC faults. Multiscale poincare mapping is a novel nonlinear tool that is further developed and modified using the normal cumulative distribution function and multiscale computing methods to capture the behavior evolution and changes in the generator’s external leakage flux signals. The healthy indicator is based on the analysis of elliptical orbit features extracted from MMSPP, established by support vector data description with parameter optimization. The effectiveness of the proposed method was implemented and verified under an experimental environment on a 100 kW DFIG platform to detect incipient inter-turn short circuits (mainly considering the two-turn ITSC) and evaluate the performance degradation (three- to eight-turn ITSC) with 0%, 50%, and 100% different load conditions. The experimental results showed the viability of the approach and fault indicator for incipient fault detection and condition assessment of the wind generator’s low inter-turn insulation faults and for relative quantification of ITSC fault severity.

Voltage sags are often manifested as the permanent or incipient faults occurred in the power system because of equipment malfunctions or failures. The incipient faults which are originally self-cleaning faults may repeatedly occur and gradually develop to a permanent fault after its first occurrence. The incipient fault detection is considered as a crucial task in predictive maintenance for power equipment such as transformers, circuit breakers, and underground cables. This paper proposes a hybrid method for incipient faults detection and classification. The proposed method firstly adopts two extraction methods and two feature measures to obtain seven peculiar features from voltage waveforms of abnormal phases recorded by power quality monitors at substations in a transmission network. Then, a feature selection method and the support vector machine combined with particle swarm optimization are applied to classify various types of incipient faults. Test results show that the proposed method contributes relatively accurate classification of incipient faults and can be employed as a useful tool for condition monitoring of major power equipment in the transmission network.

Incipient fault detection in transformers can provide early warning of electrical failure and could prevent catastrophic losses. To develop transformer incipient fault detection technique, a transformer model to simulate internal incipient faults is required. This paper presents a methodology to model internal incipient winding faults in distribution transformers. These models were implemented by combining deteriorating insulation models with an internal short circuit fault model. The internal short circuit fault model was developed using finite element analysis. The deteriorating insulation model, including an aging model and an arcing model connected in parallel, was developed based on the physical behavior of aging insulation and the arcing phenomena occurring when the insulation was severely damaged. The characteristic of the incipient faults from the simulation were compared with those from some potential experimental incipient fault cases. The comparison showed the experimentally obtained characteristic's of terminal behavior of the faulted transformer were similar to the simulation results from the incipient fault models.

In this paper, we present a synergistic approach to startup fault detection and diagnosis in gas turbine engines. The method employs statistics, signal processing and soft computing techniques in a complementary manner to address fault detection at transient conditions. Traditional turbine engine fault detection and diagnosis methods are based on engine data collected at steady-state conditions. However, insipient faults are difficult to diagnose using steady-state engine date; only engine faults that are fairly developed can be detected using conventional methods. Because incipient engine component faults are often manifested in the engine startup characteristics, we present a method of characterizing the engine transient startup using the following steps: engine sensor data during startup are recorded in time series format. The sensor profiles corresponding to "good" and "bad" engine startups are sampled using the bootstrap technique. A feature vector is extracted from these data using principal component analysis (PCA). Then, several important discriminating features are distilled from the feature vector. The features obtained from this step are then classified using neural-network-based methods. The "leave-one-out" approach to cross-validation is applied to obtain an objective evaluation of the neural network training. The proposed fault detection and diagnosis method is evaluated using actual engine startup data and the results are presented.

Incipient fault detection in a hydraulic system is a challenge in the condition monitoring community. Existing research mainly monitors abnormal working conditions in hydraulic systems by separately detecting the key working parameter, which often causes a high miss warning rate for incipient faults due to the oversight of parameter dependence. A principal component analysis provides an effective method for incipient fault detection by taking the correlation of multiple parameters into consideration, but this technique assumes the systems are Gaussian-distributed, making it invalid for a dynamic non-Gaussian system. In this paper, we combine a canonical variable analysis (CVA) and adaptive kernel density estimation (AKDE) for the early fault detection of nonlinear dynamic hydraulic systems. The collected hydraulic system data set was used to construct the typical variable space, and the state space and residual space are divided to represent the characteristics of different correlations between the two variables, which are quantitatively described using Hotelling's T2 and Q. In order to investigate the proper upper control limits, AKDE was utilised to estimate the underlying probability density functions of T2 and Q by taking the nonlinearity of the hydraulic system variables into consideration. The advantages of the proposed approach for incipient fault detection are illustrated via a marine power plant lubrication system.

Software fault detection is crucial for ensuring reliable and high-quality software systems. However, traditional fault detection methods often rely on manual inspection or rule-based techniques, which are time-consuming and prone to human errors. In this research, the researchers propose an enhanced fault detection approach using an adaptive neural transfer learning algorithm. The goal is to leverage the power of neural networks and adaptability to improve fault detection accuracy and classification performance. The problem addressed in this research is the need for more effective fault detection methods that can handle the complexities of modern software systems. Existing fault detection techniques lack adaptability and struggle to cope with diverse software scenarios. Neural networks have shown promise in pattern recognition and classification tasks, making them suitable for fault detection. However, fixed architectures and training strategy limit their performance in different software contexts. To address this problem, the research proposes an adaptive neural transfer learning algorithm for fault detection. The algorithm dynamically adjusts its neural network architecture and training process based on the characteristics of the software under test. It incorporates adaptive mechanisms, such as adjusting learning rates and regularization techniques, to optimize performance. Real-time feedback and performance evaluation during the training process drive the adaptive mechanisms. To evaluate the proposed approach, the researchers conducted a series of experiments using diverse software systems and fault scenarios. The research compared the performance of the adaptive algorithm with traditional fault detection methods, including rule-based techniques and fixed neural network architectures. Evaluation metrics such as accuracy, precision, recall, and F1 score were used. The results consistently show that the adaptive neural transfer learning algorithm outperforms existing methods, achieving higher fault detection accuracy and improved classification performance.

The self-attention mechanism has advantages in analyzing the internal characteristics of data and capturing local information. Therefore, it is applied to fault detection. Since complex industrial processes usually contain a mixture of properties, such as nonlinearity, dynamics, and non-Gaussianity, this places higher demands on fault detection methods. To further improve the performance of the self-attention mechanism for fault detection in complex industrial processes, an innovative self-attention mechanism integrating the adaptive double subspace (ISA-ADS) method is proposed for fault detection. First, the query matrix, the key matrix, and the value matrix of the self-attention mechanism are designed so that they can better focus on local information through the adaptive sample weight allocation strategy. At the same time, this design reduces the interference of nonlinear and noisy information and amplifies the impact of important fault information in the sample. Second, considering the problem of Gaussian and non-Gaussian distributions of the data and the different degrees of influence of different subspaces on the final process monitoring results, an adaptive Gaussian and non-Gaussian double subspace fault detection model based on the self-attention output matrix is established, which adaptively assigns different weights to different subspaces by determining their importance. The statistics of different subspaces are fused using the Bayesian inference and a new adaptive monitoring statistic is constructed to obtain a more accurate monitoring of the process state. Finally, the ISA-ADS is validated using the Tennessee Eastman (TE) process and the actual polyethylene production process. The results show that ISA-ADS has a low false alarm rate and a high fault detection rate, verifying the effectiveness of its fault detection performance.

In the field of industrial maintenance, early detection of gearbox faults is crucial, yet challenging, especially under varying and unseen operating conditions and types of damage. Traditional gear fault detection methods are typically designed for known operating conditions. However, in real industrial scenarios, gear fault detection is always an open-set detection, in which working conditions are commonly unseen. To overcome these limitations, this study introduces the ResNetAutoencoder Fault Detection (RAFD) method. RAFD integrates a short-time Fourier transform with a pre-trained ResNet-18 model for feature extraction and an autoencoder for feature reconstruction, trained on normal operational data. Moreover, a similarity loss is introduced to minimize the distribution gap of different working conditions. The effectiveness of the method is evaluated against traditional autoencoder and one-class support vector machine across various operational conditions, with performance metrics including AUROC, precision, recall, and F1 -score. The results demonstrate RAFD's superior performance in both in-distribution and out-of-distribution tasks, underscoring its robustness and reliability in detecting normal and faulty gearbox states.

Detection of incipient faults in post-fault systems subject to adaptive fault-tolerant control

The objective of this paper is to present the development and numerical testing of a robust fault detection and identification (FDI) system using artificial neural networks (ANNs), for incipient (slowly developing) faults occurring in process systems. The challenge in using ANNs in FDI systems arises because of one's desire to detect faults of varying severity, faults from noisy sensors, and multiple simultaneous faults. To address these issues, it becomes essential to have a learning algorithm that ensures quick convergence to a high level of accuracy. A recently developed accelerated learning algorithm, namely a form of an adaptive back propagation (ABP) algorithm, is used for this purpose. The ABP algorithm is used for the development of an FDI system for a process composed of a direct current motor, a centrifugal pump, and the associated piping system. Simulation studies indicate that the FDI system has significantly high sensitivity to incipient fault severity, while exhibiting insensitivity to sensor noise. For multiple simultaneous faults, the FDI system detects the fault with the predominant signature. The major limitation of the developed FDI system is encountered when it is subjected to simultaneous faults with similar signatures. During such faults, the inherent limitation of pattern-recognition-based FDI methodsmore » becomes apparent. Thus, alternate, more sophisticated FDI methods become necessary to address such problems. Even though the effectiveness of pattern-recognition-based FDI methods using ANNs has been demonstrated, further testing using real-world data is necessary.« less

Incipient faults would repeatedly occur in underground cables when cable insulations or splices are damaged and would eventually develop into a permanent fault. Due to the short duration of incipient faults, the conventional protection relays cannot detect this type of faults. The objective of this paper is to develop a robust incipient fault detection algorithm by using phase current. Based on morphological gradient wavelet (MGW), the proposed algorithm is to detect the fault-induced or operation-induced transients, and therefore identify the incipient faults according to the unique characteristics. It can be applied to detect both sub-cycle and multi-cycle incipient faults. The performance of the proposed algorithm is evaluated using data collected from PSCAD/EMTDC. In addition, to verify the applicability of the proposed algorithm, other disturbances caused by permanent fault, capacitor bank switching, load change, etc, are studied as well.

Quality-related fault detection is an effective way to ensure the stability of product quality and the safety of industrial processes. Quality abnormality is often caused by incipient faults, which propagate through the connectivity path among the equipment and subsystems, and eventually become significant faults that affect product quality and production efficiency. In this paper, a quality-related incipient fault detection method for large-scale industrial process is proposed. Firstly, a fault-relevant variable selection strategy based on Kullback-Leibler divergence (KLD) is proposed to obtain the optimal variable subsets of quality-related and quality-unrelated faults. Secondly, canonical variate analysis is applied to estimate the canonical variables from the collected processes data. Thirdly, considering that KLD has a high sensitivity to incipient faults, it is used to quantify the dissimilarity between the distributions of canonical vectors before and after fault occurs. Finally, Bayesian inference is employed to fuse the detection results of all sub-blocks to get an intuitive global detection result. The advantages and validity of the proposed scheme are verified based on the Tennessee Eastman process.

Modified performance-enhanced PCA for incipient fault detection of dynamic industrial processes