Organizational Environmental Protection Climate of a Rubber Products Factory in China

Abstract: The aim of this study is to reveal the relationship between 1

Timed Fault Tree Models of the China Yongwen Railway Accident

Safety is an essential requirement for railway transportation. There are many methods that have been developed to predict, prevent and mitigate accidents in this context. All of these methods have their own purpose and limitations. This paper presents a new useful analysis technique: timed fault tree analysis. This method extends traditional fault tree analysis with temporal events and fault characteristics. Timed Fault Trees (TFTs) can determine which faults need to be eliminated urgently, and it can also provide a safe time window to repair them. They can also be used to determine the time taken for railway maintenance requirements, and thereby improve maintenance efficiency, and reduce risks. In this paper, we present the features and functionality of a railway transportation system based on timed fault tree models. We demonstrate the applicability of our framework via a case study of the China Yongwen line railway accident

A hazard analysis via an improved timed colored petri net with time–space coupling safety constraint

AbstractPetri nets are graphical and mathematical tools that are applicable to many systems for modeling, simulation, and analysis. With the emergence of the concept of partitioning in time and space domains proposed in avionics application standard software interface (ARINC 653), it has become difficult to analyze time–space coupling hazards resulting from resource partitioning using classical or advanced Petri nets. In this paper, we propose a time–space coupling safety constraint and an improved timed colored Petri net with imposed time–space coupling safety constraints (TCCP-NET) to fill this requirement gap. Time–space coupling hazard analysis is conducted in three steps: specification modeling, simulation execution, and results analysis. A TCCP-NET is employed to model and analyze integrated modular avionics (IMA), a real-time, safety-critical system. The analysis results are used to verify whether there exist time–space coupling hazards at runtime. The method we propose demonstrates superior modeling of safety-critical real-time systems as it can specify resource allocations in both time and space domains. TCCP-NETs can effectively detect underlying time–space coupling hazards

Bearing remaining life prediction using Gaussian process regression with composite kernel functions

There is an urgent demand for life prediction of bearing in industry. Effective bearing degradation assessment technique is beneficial to condition based maintenance (CBM). In this paper, Gaussian Process Regression (GPR) is used for remaining bearing life prediction. Three main steps of prediction schedule are presented in details. RMS, Kurtosis and Crest factor are used for feature fusion by self-organizing map (SOM). Minimum Quantization Error (MQE) value derived from SOM is applied to represent the condition of bearing. GPR models with both single and composite covariance functions are presented. After training, new MQE value can be predicted by the GPR model according to previous data points. Experimental results show that composite kernels improve the accuracy and reduce the variance of prediction results. Compared with particle filter (PF), GPR model can predict the remaining life of bearings more accurately

Bearing remaining life prediction using Gaussian process regression with composite kernel functions

There is an urgent demand for life prediction of bearing in industry. Effective bearing degradation assessment technique is beneficial to condition based maintenance (CBM). In this paper, Gaussian Process Regression (GPR) is used for remaining bearing life prediction. Three main steps of prediction schedule are presented in details. RMS, Kurtosis and Crest factor are used for feature fusion by self-organizing map (SOM). Minimum Quantization Error (MQE) value derived from SOM is applied to represent the condition of bearing. GPR models with both single and composite covariance functions are presented. After training, new MQE value can be predicted by the GPR model according to previous data points. Experimental results show that composite kernels improve the accuracy and reduce the variance of prediction results. Compared with particle filter (PF), GPR model can predict the remaining life of bearings more accurately

Risk Quantification and Analysis of Coupled Factors Based on the DEMATEL Model and a Bayesian Network

With the developing of high integrations in large scale systems, such as aircraft and other industrial systems, there are new challenges in safety analysis due to the complexity of the mission process and the more complicated coupling characteristic of multi-factors. Aiming at the evaluation of coupled factors as well as the risk of the mission, this paper proposes a combined technology based on the Decision Making Trial and Evaluation Laboratory (DEMATEL) model and the Bayesian network (BN). After identifying and classifying the risk factors from the perspectives of humans, machines, the environment, and management, the DEMATEL technique is adopted to assess their direct and/or indirect coupling relationships to determine the importance and causality of each factor; moreover, the relationship matrix in the DEMATEL model is used to generate the BN model, including its parameterization. The inverse reasoning theory is then implemented to derive the probability, and the risk of the coupled factors is evaluated by an assessment model integrating the probability and severity. Furthermore, the key risk factors are identified based on the risk radar diagram and the Pareto rule to support the preventive measurements. Finally, an application of the take-off process of aircraft is provided to demonstrate the proposed method

Safety analysis for integrated modular avionics based on blueprints

The Integrated Modular Avionics System (IMA) has been a core technology for the new generation of aircrafts in recent years. It consists of a set of reusable and interoperable common functional modules. However, the highly coupled relationship of resources makes it difficult to identify and control dangers. As an effective and efficient way, the blueprints are used to describe and manage the IMA system. Owing to the system management functions provided by the blueprints, we can accurately determine the system resources configuration status, which is very crucial for safety analysis. In this paper, we explore the possibilities to conduct safety analysis based on blueprints. A safety analysis method based on blueprints is proposed, which applies mathematical logic to describe the logical relationship between targets and resources provided by the blueprints and uses semi-tensor product of matrix theory to simplify the logical expressions. Based on the mathematical model, we can conduct the fail safety analysis and identify resources failures that may undermine the IMA system safety