A health state assessment method for ship propulsion system based on fuzzy theory and variable weight theory

It is hard to determine the equipment weight in the ship propulsion system health status evaluation. A device weight determination method based on fuzzy theory and variable weight theory is presented. In this method, expert knowledge is used to determine the initial weight of each device; variable weights theory is used to appropriately adjust devices weights combining with the actual health status of each device. Simulation analysis results show that the proposed propulsion system integrated status assessment method could reasonably reflect actual status, which proves it to be scientific and valid in engineering application

A rolling bearing health status assessment method based on support vector data description

This paper investigates the application of the entropy parameter and support vector data description (SVDD) on signal processing and rolling bearing health status assessing. On this basis, a novel method for mechanical health status assessment method based on entropy and SVDD is presented, which uses the entropy parameters to reflect the health status of rolling bearing life cycle, and then these parameters are input into SVDD to accomplish health status assessment. The experimental result of the proposed method to health status assessing of the rolling bearing shows that this method can extract the health status features, which have better ability of reflecting the status degeneration, accordingly solve the problem of health status assessment with poor data

Rolling element bearings health status indictor analysis

According to the vibration mechanism of ship gas turbine rolling element bearings common failure modes, the variation of the common indicators during the rolling element bearings health status degradation process is analyzed, and the reflection ability of the various indicators is studied based on the consistency and sensitivity. The results show that the Root-Mean-Square value, Peak-Peak value, Wavelet Energy Spectrum Entropy and Singular Spectrum Entropy can effectively reflect the health state change of rolling element bearings

Data-Driven Machine Learning for Fault Detection and Diagnosis in Nuclear Power Plants: A Review

Data-driven machine learning (DDML) methods for the fault diagnosis and detection (FDD) in the nuclear power plant (NPP) are of emerging interest in the recent years. However, there still lacks research on comprehensive reviewing the state-of-the-art progress on the DDML for the FDD in the NPP. In this review, the classifications, principles, and characteristics of the DDML are firstly introduced, which include the supervised learning type, unsupervised learning type, and so on. Then, the latest applications of the DDML for the FDD, which consist of the reactor system, reactor component, and reactor condition monitoring are illustrated, which can better predict the NPP behaviors. Lastly, the future development of the DDML for the FDD in the NPP is concluded

Development of Approaches to Common Cause Dependencies with Applications to Multi-Unit Nuclear Power Plant

The term “common cause dependencies” encompasses the possible mechanisms that directly compromise components performances and ultimately cause degradation or failure of multiple components, referred to as common cause failure (CCF) events. The CCF events have been a major contributor to the risk posed by the nuclear power plants and considerable research efforts have been devoted to model the impacts of CCF based on historical observations and engineering judgment, referred to as CCF models. However, most current probabilistic risk assessment (PRA) studies are restricted to single reactor units and could not appropriately consider the common cause dependencies across reactor units. Recently, the common cause dependencies across reactor units have attracted a lot of attention, especially following the 2011 Fukushima accident in Japan that involved multiple reactor unit damages and radioactive source term releases. To gain an accurate view of a site's risk profile, a site-based risk metric representing the entire site rather than single reactor unit should be considered and evaluated through a multi-unit PRA (MUPRA). However, the multi-unit risk is neither formally nor adequately addressed in either the regulatory or the commercial nuclear environments and there are still gaps in the PRA methods to model such multi-unit events. In particular, external events, especially seismic events, are expected to be very important in the assessment of risks related to multi-unit nuclear plant sites. The objective of this dissertation is to develop three inter-related approaches to address important issues in both external events and internal events in the MUPRA. 1) Develop a general MUPRA framework to identify and characterize the multi-unit events, and ultimately to assess the risk profile of multi-unit sites. 2) Develop an improved approach to seismic MUPRA through identifying and addressing the issues in the current methods for seismic dependency modeling. The proposed approach can also be extended to address other external events involved in the MUPRA. 3) Develop a novel CCF model for components undergoing age-related degradation by superimposing the maintenance impacts on the component degradation evolutions inferred from condition monitoring data. This approach advances the state-of-the-art CCF analysis in general and assists in the studies of internal events of the MUPRA

Method of constructing braid group representation and entanglement in a Yang-Baxter sysytem

In this paper we present reducible representation of the $n^{2}$ braid group representation which is constructed on the tensor product of n-dimensional spaces. By some combining methods we can construct more arbitrary $n^{2}$ dimensional braiding matrix S which satisfy the braid relations, and we get some useful braiding matrix S. By Yang-Baxteraition approach, we derive a $9\times9$ unitary $\breve{R}$ according to a $9\times9$ braiding S-matrix we have constructed. The entanglement properties of $\breve{R}$-matrix is investigated, and the arbitrary degree of entanglement for two-qutrit entangled states can be generated via $\breve{R}(\theta, \phi_{1},\phi_{2})$-matrix acting on the standard basis.Comment: 9 page

Precise detection and localization of R-peaks from ECG signals

Heart rate variability (HRV) is derived from the R-R interval, which depends on the precise localization of R-peaks within an electrocardiogram (ECG) signal. However, current algorithm assessment methods prioritize the R-peak detection’s sensitivity rather than the precision of pinpointing the exact R-peak positions. As a result, it is of great value to develop an R-peak detection algorithm with high-precision R-peak localization. This paper introduces a novel R-peak localization algorithm that involves modifications to the well-established Pan-Tompkins (PT) algorithm. The algorithm was implemented as follows. First, the raw ECG signal X(i) was band-pass filtered (5–35 Hz) to obtain a preprocessed signal Y(i). Second, Y(i) was squared to enhance the QRS complex, followed by a 5 Hz low-pass filter to obtain the QRS envelope, which was transformed into a window signal W(i) by dynamic threshold with a minimum width of 200 ms to mark the QRS complex. Third, Y(i) was used to generate QRS template T(n) automatically, and then the R-peak was identified by a template matching process to find the maximum absolute value of all cross-correlation values between T(n) and Y(i). The proposed algorithm achieved a sensitivity (SE) of 99.78%, a positive prediction value (PPV) of 99.78% and data error rate (DER) of 0.44% in R-peak localization for the MIT-BIH Arrhythmia database. The annotated-detected error (ADE), which represents the error between the annotated R-peak location and the detected R-peak location, was 8.35 ms for the MIT-BIH Arrhythmia database. These results outperformed the results obtained using the classical Pan-Tompkins algorithm which yielded an SE of 98.87%, a PPV of 99.14%, a DER of 1.98% and an ADE of 21.65 ms for the MIT-BIH Arrhythmia database. It can be concluded that the algorithm can precisely detect the location of R-peaks and may have the potential to enhance clinical applications of HRV analysis.</p

Precise detection and localization of R-peaks from ECG signals

Heart rate variability (HRV) is derived from the R-R interval, which depends on the precise localization of R-peaks within an electrocardiogram (ECG) signal. However, current algorithm assessment methods prioritize the R-peak detection’s sensitivity rather than the precision of pinpointing the exact R-peak positions. As a result, it is of great value to develop an R-peak detection algorithm with high-precision R-peak localization. This paper introduces a novel R-peak localization algorithm that involves modifications to the well-established Pan-Tompkins (PT) algorithm. The algorithm was implemented as follows. First, the raw ECG signal X(i) was band-pass filtered (5–35 Hz) to obtain a preprocessed signal Y(i). Second, Y(i) was squared to enhance the QRS complex, followed by a 5 Hz low-pass filter to obtain the QRS envelope, which was transformed into a window signal W(i) by dynamic threshold with a minimum width of 200 ms to mark the QRS complex. Third, Y(i) was used to generate QRS template T(n) automatically, and then the R-peak was identified by a template matching process to find the maximum absolute value of all cross-correlation values between T(n) and Y(i). The proposed algorithm achieved a sensitivity (SE) of 99.78%, a positive prediction value (PPV) of 99.78% and data error rate (DER) of 0.44% in R-peak localization for the MIT-BIH Arrhythmia database. The annotated-detected error (ADE), which represents the error between the annotated R-peak location and the detected R-peak location, was 8.35 ms for the MIT-BIH Arrhythmia database. These results outperformed the results obtained using the classical Pan-Tompkins algorithm which yielded an SE of 98.87%, a PPV of 99.14%, a DER of 1.98% and an ADE of 21.65 ms for the MIT-BIH Arrhythmia database. It can be concluded that the algorithm can precisely detect the location of R-peaks and may have the potential to enhance clinical applications of HRV analysis.</p