Incipient fault detection and isolation in a PWR plant using principal component analysis

Degradation monitoring is of paramount importance to safety and reliability of aircraft operations and also for timely maintenance of its critical components. This two-part paper formulates and validates a novel methodology of degradation monitoring of aircraft gas turbine engines with emphasis on detection and isolation of incipient faults. In a complex system with multiple interconnected components (e.g. an aircraft engine), fault isolation becomes a crucial task because of possible input—output and feedback interactions among the individual components. This paper, which is the first of two parts, presents the underlying concepts of fault detection and isolation (FDI) in complex dynamical systems. The FDI algorithms are formulated in the setting of symbolic dynamic filtering (SDF) that has been recently reported in literature. The underlying concept of SDF is built upon the principles of symbolic dynamics, statistical pattern recognition, and information theory. In addition to abrupt large faults, the SDF-based algorithms are capable of detecting slowly evolving anomalies (i.e. deviations from the nominal behaviour) based on analysis of time series data of critical process variables of different engine components. The second part, which is a companion paper, validates the concept, laid out in the first part, on the simulation test bed of a generic two-spool turbofan aircraft engine model for detection and isolation of incipient faults.

Thermal embrittlement of some cast austenitic stainless steels (CASS) occurs at reactor operating temperatures leading to very low fracture toughness. Because of their low aged toughness with high variability, flaw evaluations of CASS material need to be established with an understanding of the materials aged condition, especially since most U.S. pressurized water reactor (PWR) nuclear plants have been given plant life extensions for 60-year operation. A flaw evaluation procedure for CASS materials is presented here using a new statistical model developed to predict the toughness of fully aged CASS using the materials' chemical compositions. In this procedure, the dimensionless-plastic-zone-parameter (DPZP) analysis is used to determine when limit-load is applicable and also approximate the elastic-plastic correction factor (Z-factor) to predict the failure stress for CASS pipe/fittings with a circumferential surface crack. The procedure was validated against several CF8m pipe test results which include various pipe diameters, crack sizes, ferrite contents, failure modes. The as-developed flaw evaluation procedure was also used to determine the Z-factors for four different pipe diameters for a database of 274 pipe/elbows in U.S. PWR plants –solving 1096 sample problems to understand what range of Z-factors in U.S. PWR plants (for CF8m CASS materials). Finally, the applicability of the CF8m-based statistical model for use with CF3 and CF8 CASS materials was also verified with available test results. The outcome of this work has been implemented in an ASME boiler and pressure vessel (BPV) code case and has been approved in 2020 as Code Case N-906.

The incipient fault detection of a complex industrial process is a challenging problem for traditional dynamic detection methods. Traditional dynamic detection methods usually decouple the correlations among the variables and dynamic correlations simultaneously, which makes the two types of correlations mixed and may lead to performance deterioration in long-sequence dynamic detection. Some incipient faults may not change the amplitudes of process variables but change the long-sequence dynamic features. Based on the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$T^{2}$</tex-math> </inline-formula> statistic and matrix multiplication transformation ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$T^{2}$</tex-math> </inline-formula> S-MMT), traditional dynamic detection methods can detect many faults effectively. However, the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$T^{2}$</tex-math> </inline-formula> S-MMT can not effectively detect some incipient faults due to the above two types of correlations mixed. In order to overcome the shortcomings of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$T^{2}$</tex-math> </inline-formula> S-MMT and improve the detection ability of some incipient faults, this paper proposes a fault detection method based on the dynamic k-nearest neighbor model and Dual Control Chart (DKNN-DCC), which can improve the incipient fault detection performance by using long-sequence dynamic detection. The proposed method is verified by the Tennessee Eastman (TE) process and the continuously stirred tank reactor (CSTR) process. The experimental results show the effectiveness of the proposed method in incipient fault detection compared with traditional dynamic detection methods. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This paper presents a novel incipient fault detection method, which directly mines the long-sequence dynamic abnormal information from the process variable and overcomes the problem of some abnormal information being submerged in the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$T^{2}$</tex-math> </inline-formula> statistic calculated based on the matrix multiplication transformation. The proposed method can detect incipient faults that are not easily detected by some traditional methods and can help operators find the abnormal and avoid more serious losses. The structure of the proposed method jumps out of the frameworks of traditional dynamic detection methods, which is feasible to apply to different stable industrial processes.

To monitor the dynamics and non-stationarity inherent in industrial processes, we propose a novel incipient fault detection and isolation scheme grounded in a probabilistic perspective, using the Cauchy–Schwarz (CS) divergence. Our innovation lies in the utilization of marginal CS divergence for incipient fault detection and the conditional CS divergence for fault isolation. This approach neither require prior parametric assumptions about the underlying data distribution nor depend on historical fault data, while simultaneously providing explanatory diagnostics. Beyond this, we develop a change point detection-base diagnosis technique for practical engineering applications. This online process monitoring technique guarantees timely intervention to uphold process stability and safety. We demonstrate the compelling performance, higher detection rate and lower alarm rate, of the CS divergence over prevalent divergence-based approaches, such as Kullback–Leibler divergence, Wasserstein distance and Mahalanobis distance. We also illustrate the explanatory insights offered by conditional CS divergence in fault isolation on synthetic data, benchmarks of continuous stirred-tank reactor process and continuous stirred-tank heater process and even a real-world continuous catalytic reforming process. Code of this CS divergence based fault detection and isolation is available at https://github.com/Feiya-Lv/Incipient-Fault-Detection-and-Isolation-with-Cauchy--Schwarz-Divergence.

This paper addresses the problem of incipient fault detection and isolation for a class of Lipschitz nonlinear systems with multiple sensor biases and explores further results of total measurable fault information residual (ToMFIR). Firstly, state and output transformations are introduced to transform the original system into two subsystems. Since the effects of system disturbances on the residuals are completely decoupled, the incipient faults in the second subsystem can be detected by constructing ToMFIR. Further, sliding-mode observers (SMOs) based fault isolation scheme is designed to guarantee the isolation of multiple sensor faults even if they occur at the same time, which is the main contribution of this paper. Finally, application results conducted on a CRH2 high-speed railway traction motor are given to illustrate the effectiveness of the proposed approach.

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.

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.

The primary purpose of this research is to examine the stress corrosion cracking (SCC) resistance of Alloy 800NG in pressurized water reactor (PWR) primary water and pressurized heavy water reactor (PHWR) primary water. Rates of SCC growth of 20% cold-worked (CW) Alloy 800NG measured over the temperature range between 270°C and 360°C were compared with previously reported results for 20% CW Alloy TT690 and 20% CW Alloy 600 in order to consider which material is the most SCC resistant among materials presently being used for steam generator (SG) tubing worldwide. The secondary purpose is to examine the effect of chromium addition on SCC growth in PWR primary water of a series of alloys based on the Alloy 800 composition. SCC growth measurements were performed in PWR primary water over the chromium concentration range from 16% to 27% to obtain fundamental knowledge useful for considering a future alternative SCC-resistant material for SG tubing in extended life PWRs and PHWRs. The third objective is to examine the rate of cavity formation of 20% CW Alloy 800NG to obtain basic knowledge of one possible mechanism for SCC initiation after long-term operation. Measured rates of cavity formation in 20% CW Alloy 800NG were compared with previously reported results of 20% CW Alloy TT690 to compare the rate of SCC initiation caused by cavity formation. Four important patterns were observed. First, excellent SCC growth resistance was observed for 20% CW Alloy 800NG compared to 20% CW Alloy TT690 at 320°C, 340°C, and 360°C. Second, an inverse temperature dependence on SCC growth was observed in Alloy 800NG. The rate of SCC growth increased with decreasing temperature which was completely different from the trend for Alloy 600. Third, a significant beneficial effect by chromium addition in 800 series alloys on SCC growth resistance was observed in PWR primary water in the operating temperature range of PWRs and PHWRs. The rate of SCC growth decreased with increasing chromium concentration in the chromium concentration range between 16% and 27% chromium at 270°C, 290°C, and 320°C. However, no beneficial effect of chromium addition in these alloys was observed at 340°C and 360°C. Finally, a more than 10 times slower rate of cavity formation was observed in 20% CW Alloy 800NG than for 20% CW Alloy TT690. Results suggested that because of cavity formation, a more than 10-fold faster crack initiation occurred in Alloy TT690 than in Alloy 800NG. Further, carbide coverage and grain size significantly affected the rate of cavity formation. Detailed and comprehensive studies of long-term SCC initiation are necessary to ensure the future reliability of life-extended PWRs and PHWRs.

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.

The basic safety design philosophy of Mitsubishi pressurized water reactors (PWRs) is discussed and compared with the British PWR. PWR plants are designed in accordance with the Japanese regulatory guidelines which are similar to American and International Atomic Energy Agency (IAEA) safety criteria and are based on defence-in-depth principles. The high reliability of nuclear power plants is especially emphasized in Mitsubishi PWRs, and this has been demonstrated by the good operating experience of PWR plants in Japan. The safety system designs of six key items, which were discussed in the recent review of overseas designs by British utilities, are addressed to show the difference in the design philosophy between the United Kingdom and Japan.

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

The brake system is a key system for the safe operation and stopping of trains. As a core component of brake systems, the brake actuation unit (BAU) is essential for slowing down or stopping trains, and faults in the BAU will affect the safety and efficiency of train operation. In order to detect and locate faults in the BAU in time, a fault detection and isolation (FDI) strategy, based on mutual residuals (MRs), principal component analysis (PCA) and improved reconstruction-based contribution plots (IRBCP), was proposed. Firstly, the structural composition and working principle of the BAU were introduced, and its typical failure modes and effects were analyzed. Secondly, considering that the fault detection threshold is not easy to determine due to the variable operating conditions of the BAU, the steady-state fault feature based on MR was extracted. Thirdly, fault detection and isolation were realized based on PCA and the IRBCP algorithm. Finally, by using the fault injection method, case studies on test-rig experiment data of brake systems were conducted; the fault detection rate was 99% and the effectiveness of the proposed strategy was validated by the test data. The proposed strategy shows fast computing ability, and is suitable for systems with dynamic time-varying and nonlinear characteristics.

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.

An experiment focusing on nitrogen gas behavior during reflux cooling in a pressurized water reactor (PWR) was performed with the rig of safety assessment/large scale test facility (ROSA/LSTF) at Japan Atomic Energy Agency. The test conditions were made such as the constant core power of 0.7% of the volumetric-scaled PWR nominal power and the primary pressure of lower than 1 MPa, unlike a previous related test with the LSTF. The steam generator (SG) secondary-side collapsed liquid level was maintained at a certain liquid level above the SG tube height. Nitrogen gas was injected stepwise into each SG inlet plenum at a certain constant amount. The primary pressure and the SG U-tube fluid temperatures were greatly dependent on the amount of nitrogen gas accumulated in the SG U-tubes. Nitrogen gas accumulated from the outlet towards the inlet of the SG U-tubes. Non-uniform flow behavior was observed among the SG U-tubes with nitrogen gas ingress. The RELAP5/MOD3.3 code indicated remaining problems in the predictions of the primary pressure and the SG U-tube fluid temperatures after nitrogen gas inflow.