Operational time-series data modeling via LSTM network integrating principal component analysis based on human experience

Today’s information technologies involve increasingly intelligent systems, which come at the cost of increasingly complex equipment. Modern monitoring systems collect multi-measuring-point and long-term data which make equipment health prediction a “big data” problem. It is difficult to extract information from such condition monitoring data to accurately estimate or predict health statuses. Deep learning is a powerful tool for big data processing that is widely utilized in image and speech recognition applications, and can also provide effective predictions in industrial processes. This paper proposes the Long Short-term Memory Integrating Principal Component Analysis based on Human Experience (HEPCA-LSTM), which uses operational time-series data for equipment health prognostics. Principal component analysis based on human experience is first conducted to extract condition parameters from the condition monitoring system. The long short-term memory (LSTM) framework is then constructed to predict the target status. Finally, a dynamic update of the prediction model with incoming data is performed at a certain interval to prevent any model misalignment caused by the drifting of relevant variables. The proposed model is validated on a practical case and found to outperform other prediction methods. It utilizes a powerful deep learning analysis method, the LSTM, to fully process big condition monitoring series data; it effectively extracts the features involved with human experience and takes dynamic updates into consideration.     

智能化救援监控系统在煤矿安全生产中的应用

针对煤矿发生事故后传统救援监控系统无法实时对井下人员进行动态定位，导致矿井救援盲目性大、救援效率差、救援难度大等技术难题，为了进一步提高煤矿救援效率，通过技术研究，设计了一套以通信基站为核心的智能化救援监控系统，分析了该系统结构组成、工作原理，通过在担水沟煤矿井下实际应用效果来看，智能化救援监控系统对人员定位精准度达95%，实现人员动态位置三维成像，救援效率提高至80%以上，有效缩短了煤矿事故救援时间，取得了显著应用成效。     

Preventive replacement policies with time of operations,mission durations,minimal repairs and maintenance triggering approaches

When a mission arrives at a random time and lasts for a duration, it becomes an interesting problem to plan replacement policies according to the health condition and repair history of the operating unit, as the reliability is required at mission time and no replacement can be done preventively during the mission duration. From this viewpoint, this paper proposes that effective replacement policies should be collaborative ones gathering data from time of operations, mission durations, minimal repairs and maintenance triggering approaches. We firstly discuss replacement policies with time of operations and random arrival times of mission durations, model the policies and find optimum replacement times and mission durations to minimize the expected replacement cost rates analytically. Secondly, replacement policies with minimal repairs and mission durations are discussed in a similar analytical way. Furthermore, the maintenance triggering approaches, i.e., replacement first and last, are also considered into respective replacement policies. Numerical examples are illustrated when the arrival time of the mission has a gamma distribution and the failure time of the unit has a Weibull distribution. In addition, simple case illustrations of maintaining the production system in glass factories are given based on the assumed data.     

Superior foetal electrocardiogram signal elicitation using a novel artificial intelligent Bayesian methodology

Innumerable casualties due to intrauterine hypoxia are a major worry during prenatal phase besides advanced patient monitoring with latest science and technology. Hence, the analysis of foetal electrocardiogram (fECG) signals is very vital in order to evaluate the foetal heart status for timely recognition of cardiac abnormalities. Regrettably, the latest technology in the cutting edge field of biomedical signal processing does not seem to yield the desired quality of fECG signals required by physicians, which is the major cause for the pathetic condition. The focus of this work is to extort non-invasive fECG signal with highest possible quality with a motive to support physicians in utilizing the methodology for the latest intrapartum monitoring technique called STAN (ST analysis) for forecasting intrapartum foetal hypoxia. However, the critical quandary is that the non-invasive fECG signals recorded from the maternal abdomen are affected by several interferences like power line interference, baseline drift interference, electrode motion interference, muscle movement interference and the maternal electrocardiogram (mECG) being the dominant interference. A novel hybrid methodology called BANFIS (Bayesian adaptive neuro fuzzy inference system) is proposed. The BANFIS includes a Bayesian filter and an adaptive neuro fuzzy filter for mECG elimination and non-linear artefacts removal to yield high quality fECG signal. Kalman filtering frame work has been utilized to estimate the nonlinear transformed mECG component in the abdominal electrocardiogram (aECG). The adaptive neuro fuzzy filter is employed to discover the nonlinearity of the nonlinear transformed version of mECG and to align the estimated mECG signal with the maternal component in the aECG signal for annulment. The outcomes of the investigation by the proposed BANFIS system proved valuable for STAN system for efficient prediction of foetal hypoxia.     

A novel approach to adaptive relational association rule mining

The paper focuses on the adaptive relational association rule mining problem. Relational association rules represent a particular type of association rules which describe frequent relations that occur between the features characterizing the instances within a data set. We aim at re-mining an object set, previously mined, when the feature set characterizing the objects increases. An adaptive relational association rule method, based on the discovery of interesting relational association rules, is proposed. This method, called ARARM (Adaptive Relational Association Rule Mining) adapts the set of rules that was established by mining the data before the feature set changed, preserving the completeness. We aim to reach the result more efficiently than running the mining algorithm again from scratch on the feature-extended object set. Experiments testing the method's performance on several case studies are also reported. The obtained results highlight the efficiency of the ARARM method and confirm the potential of our proposal.     

Change detection in synthetic aperture radar images based on unsupervised artificial immune systems

In this paper, we propose a novel change detection method for synthetic aperture radar images based on unsupervised artificial immune systems. After generating the difference image from the multitemporal images, we take each pixel as an antigen and build an immune model to deal with the antigens. By continuously stimulating the immune model, the antigens are classified into two groups, changed and unchanged. Firstly, the proposed method incorporates the local information in order to restrain the impact of speckle noise. Secondly, the proposed method simulates the immune response process in a fuzzy way to get an accurate result by retaining more image details. We introduce a fuzzy membership of the antigen and then update the antibodies and memory cells according to the membership. Compared with the clustering algorithms we have proposed in our previous works, the new method inherits immunological properties from immune systems and is robust to speckle noise due to the use of local information as well as fuzzy strategy. Experiments on real synthetic aperture radar images show that the proposed method performs well on several kinds of difference images and engenders more robust result than the other compared methods.     

A probabilistic load shedding concept considering highly volatile local generation

Automatic load shedding is the ultimate countermeasure against imbalance in a power system and can effectively help preventing large blackouts. Taking into account a high penetration of renewable energy sources (RES) in the distribution grid, a clear distinction between load and generation at the PCC becomes increasingly more difficult. For that reason an adaptation of frequency relay parameters and their locations of installation are necessary. In Europe this is rest on a multi-step plan based on values such as the yearly peak load. In this paper a novel probabilistic method for automatic load shedding is presented that uses the average values instead of peak values for load shedding. Its applicability is verified by a dynamic power system model that was developed to compare the classical and novel probabilistic load shedding principle. The method is verified using data from a German TSO.     

Study of MW-scale biogas-fed SOFC-WGS-TSA-PEMFC hybrid power technology as distributed energy system: Thermodynamic,exergetic and thermo-economic evaluation

Advanced biogas power generation technology has been attracting attentions, which contributes to the waste disposal and the mitigation of greenhouse gas emissions. This work proposes and models a novel biogas-fed hybrid power generation system consisting of solid oxide fuel cell, water gas shift reaction, thermal swing adsorption and proton exchange membrane fuel cell (SOFC-WGS-TSA-PEMFC). The thermodynamic, exergetic, and thermo-economic analyses of this hybrid system for power generation were conducted to comprehensively evaluate its performance. It was found that the novel biogas-fed hybrid system has a gross energy conversion efficiency of 68.63% and exergy efficiency of 65.36%, indicating high efficiency for this kind of hybrid power technology. The market sensitivity analysis showed that the hybrid system also has a low sensitivity to market price fluctuation. Under the current subsidy level for the distributed biogas power plant, the levelized cost of energy can be lowered to 0.02942 $/kWh for a 1 MW scale system. Accordingly, the payback period and annual return on investment can reach 1.4 year and about 20%, respectively. These results reveal that the proposed hybrid system is promising and economically feasible as a distributed power plant, especially for the small power scale (no more than 2 MW).     

An interpretative structural modeling based network reconfiguration strategy for power systems

The network reconfiguration is an important stage of restoring a power system after a complete blackout or a local outage. Reasonable planning of the network reconfiguration procedure is essential for rapidly restoring the power system concerned. An approach for evaluating the importance of a line is first proposed based on the line contraction concept. Then, the interpretative structural modeling (ISM) is employed to analyze the relationship among the factors having impacts on the network reconfiguration. The security and speediness of restoring generating units are considered with priority, and a method is next proposed to select the generating unit to be restored by maximizing the restoration benefit with both the generation capacity of the restored generating unit and the importance of the line in the restoration path considered. Both the start-up sequence of generating units and the related restoration paths are optimized together in the proposed method, and in this way the shortcomings of separately solving these two issues in the existing methods are avoided. Finally, the New England 10-unit 39-bus power system and the Guangdong power system in South China are employed to demonstrate the basic features of the proposed method.     

基于学生群体心理测试系统的设计

心理测试的根本目的是为了让人们在测试过程中，产生某些特定行为，即个体对测试题目的反应，并根据这些行为反应来推论其相应的心理特性。在一定程度上帮助我们了解人类的心理活动，并从中得到帮助。因此，本文对基于学生群体心理测试系统的设计进行深入研究。