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Plastic scintillator detectors are widely utilized in radiation measurement due to their unique characteristics such as fast decay time, low production cost, availability for large-scale production, and tissue equivalence. However, their limited photoelectric absorption cross-section and poor energy resolution make it challenging to accurately detect γ-ray photopeaks. The absence of high-energy γ-ray photopeaks poses a challenge in the energy calibration of plastic scintillator detectors, which is the foundation for radiation energy detection. Nevertheless, it is feasible to calibrate a plastic scintillator detector using the Compton peaks. Numerous studies on the plastic scintillator detector energy calibration based on the Monte Carlo method were reported, but most of them cannot directly simulate energy spectra or calculate the energy values of Compton peaks. An energy calibration method combined with optical simulation was proposed to overcome this obstacle in this paper. This method describes physical processes in plastic scintillator detectors more comprehensively than energy calibration methods based on the regular Monte Carlo simulation. Furthermore, it does not rely on empirical formulas to determine energy resolution. An optical model of the plastic scintillator detector built with GEANT4 10.7 was used to describe the scintillation properties of the plastic scintillator, the properties of optical surfaces, and the photoelectric conversion and photoelectron multiplication processes of the photomultiplier tube. Thus, this model could accurately simulate the energy deposition of γ-rays, the transportation and collection of the scintillation light in the scintillator, as well as the photoelectric conversion process of the scintillation light and photoelectron multiplication process at the photomultiplier tube. These allow for an accurate simulation of the experimental energy spectrum. The simulated energy spectra were calibrated using the photopeaks of low-energy γ-rays. Since the simulated energy spectra are reasonable approximations of experimental energy spectra, the energy values of the Compton peaks in the experimental spectra are the same as Compton peaks in the calibrated simulated energy spectra. Two radiation sources of 137Cs and 60Co were used to calibrate the plastic scintillator detector in the experiment based on this method, and the energy calibration of the plastic scintillator detector was completed using Compton peaks of 137Cs and 60Co. The relative error of the Compton peak energy resolution for 137Cs between the simulated energy spectrum and the calibrated experimental energy spectrum is 0.70%. For 60Co, the value is 0.91%. Either value is less than 1%, which in turn verifies the agreement between simulated energy spectra and experimental energy spectra, and also demonstrates the reliability of the energy calibration result.

Aiming at the problem to difficultly identify the weak links of the cellular manufacturing system, a method for analyzing the importance measure based on the time series is proposed. The method extends the traditional importance measure calculation method at a specific time to the equipment importance measure in a specific time period, and the time series relationship among the equipment reliability, manufacturing cell reliability and system reliability is mined. It uses the slope of linear fitting to evaluate the importance measure of the equipment, and improves the identification ability of key equipment in the manufacturing process of the cellular manufacturing system. Through the sensitivity analysis of equipment degradation parameters, the change rule of the equipment importance measure when the equipment degradation parameters change is summarized, and the effectiveness of the time series importance measure in the identification of the key equipment and key manufacturing cells is verified.

Fully mechanized mining equipment is gradually becoming larger, more complex and more intelligent. The traditional equipment management methods of regular maintenance and post maintenance are no longer able to meet the high reliability requirements of equipment operation in coal mine intelligent construction. Therefore, studying the relevant theories and technologies of fully mechanized equipment health status assessment has great practical significance for coal mine intelligent mining. This paper proposes the scope definition of fully mechanized mining equipment health status assessment and the fully mechanized mining equipment health status assessment process. This paper summarizes the research status and development trends of comprehensive mining equipment health status assessment methods from four aspects: signal acquisition, feature extraction and fusion, health status level classification, and health status assessment model establishment. The current challenges faced by fully mechanized mining equipment health status assessment related technologies are analyzed from aspects such as signal acquisition and sensor optimization layout, data processing and feature extraction, establishment of health status assessment models, and application of fully mechanized mining equipment status assessment. In response to the current research status and challenges mentioned above, the development trend of fully mechanized mining equipment health status assessment technology is discussed from the aspects of improving data collection schemes and fault mechanism research methods, building high-performance big data computing platforms, establishing deep learning assessment models, researching dynamic evaluation models for fully mechanized mining equipment health status, and developing fully mechanized mining equipment health status assessment systems. It is pointed out that in the process of coal mine intelligence, it is necessary to ensure that the theoretical research, algorithm development, and engineering application of fully mechanized mining equipment health status assessment go hand in hand.

To meet the requirements of safety control during the fuel loading and refueling of nuclear reactors, a reactor first loading monitoring system was developed, and the related mechanical structure and electrical structure design and manufacturing of the system were completed including 3He proportional probe and pulse counter device, etc. The probe assembly adopts a new structure. The mechanical structure is a combination of a bridle structure waterproof connector, a waterproof tube and a load-bearing multi-core shielded cable. A fast charge sensitive preamplifier and a high-voltage module were integrated in the probe. The pulse counting device consist of several NIM modular plug-in units is mounted on a NIM rack mounting unit and a cabinet. The system contains four measurement channels and an acousto-optic alarm channel, which can realize functions such as counting rate display and over-threshold acousto-optic alarm. The tests show the system meets the requirements for neutron flux rate monitoring during nuclear reactor fuel loading and has been applied to unit 5 and unit 6 of Hongyanhe Nuclear Power Plant.

For the remaining useful life (RUL) prediction of rolling bearing under strong background noise, it is hard to get accurate results based on the non-stationary vibration signals because of complex degradation characteristics and difficult extraction of key features. The framework of RUL prediction for rolling bearing is established by integrating multi-domain mixed features and temporal convolutional network (TCN). The variational mode decomposition method based on the dung beetle optimization algorithm is developed to reduce signal noise by determining the optimal parameters adaptively. To construct a health indicator of rolling bearing effectively, an isometric feature mapping algorithm is introduced to reduce the dimensionality of multi-domain mixed features, integrating time-domain, frequency-domain, and entropy features of vibration signals under non-stationary and nonlinear conditions. By considering the advantages of a multi-head attention mechanism (MA) and bidirectional gated recurrent unit (BiGRU), a TCN-based multi-head attention and bidirectional gate (TCNMABG) is developed to predict the RUL of rolling bearing accurately, whose detailed implementation process of TCNMABG is described based on XJTU-SY dataset. To verify the performance of TCNMABG, the FEMTO-ST dataset is introduced to perform the numerical experiments, and the results show that prediction error is reduced by 65.96% on average. [ABSTRACT FROM AUTHOR]

In nonrepairable multifunctional systems, the lost function of a component can be restored by the same function from another component; therefore, the activation mechanism of redundant functions illustrates that multifunctional systems have resilience features. This study evaluates the resilience of multifunctional systems and analyzes the properties of system resilience first. To determine the optimal start-up strategy, a resilience-oriented start-up strategy optimization model for onsite monitoring systems (OMSs) is established to maximize system resilience under a limited budget. In this study, real-time reliability is regarded as the system performance to evaluate the system resilience, and a two-stage local search based genetic algorithm (TLSGA) is proposed to solve the resilience optimization problem. The results of our numerical experiments show that the TLSGA can more effectively solve the problems for OMSs, with high function failure rates and low component failure rates compared with classical genetic algorithms under 48 systems. Moreover, the optimal combinations of unmanned aerial vehicles (UAVs) for an OMS under a limited budget shows that UAVs with a higher carrying capacity should be given priority for selection. Therefore, this study provides an effective solution for determining the optimal start-up strategy to maximize the resilience of OMSs, which is beneficial for OMS configuration. [ABSTRACT FROM AUTHOR]