Abstract. China's new "Twelfth Five-Year Plan" set a target for total NO x emission reduction of 10 % for the period of 2011-2015. Heavy-duty diesel vehicles (HDDVs) have been considered a major contributor to NO x emissions in China. Beijing initiated a comprehensive vehicle test program in 2008. This program included a sub-task for measuring onroad emission profiles of hundreds of HDDVs using portable emission measurement systems (PEMS). The major finding is that neither the on-road distance-specific (g km −1 ) nor brake-specific (g kWh −1 ) NO x emission factors for diesel buses and heavy-duty diesel trucks improved in most cases as emission standards became more stringent. For example, the average NO x emission factors for Euro II, Euro III and Euro IV buses are 11.3 ± 3.3 g km −1 , 12.5 ± 1.3 g km −1 , and 11.8 ± 2.0 g km −1 , respectively. No statistically significant difference in NO x emission factors was observed between Euro II and III buses. Even for Euro IV buses equipped with SCR systems, the NO x emission factors are similar to Euro III buses. The data regarding real-time engine performance of Euro IV buses suggest the engine certification cycles did not reflect their real-world operating conditions. These new on-road test results indicate that previous estimates of total NO x emissions for HDDV fleet may be significantly underestimated. The new estimate in total NO x emissions for the Beijing HDDV fleet in 2009 is 37.0 Gg, an increase of 45 % compared to the previous study. Further, we estimate that the total NO x emissions for the na-
China's new "Twelfth Five-Year Plan" set a target for total NOx emission reduction of 10% for the period of 2011–2015. Heavy-duty diesel vehicles (HDDVs) have been considered a major contributor to NOx emissions in China. Beijing initiated a comprehensive vehicle test program in 2008. This program included a sub-task for measuring on-road emission profiles of hundreds of HDDVs using portable emission measurement systems (PEMS). The major finding is that neither the on-road distance-specific (g km −1) nor brake-specific (g kW h−1) NOx emission factors for diesel buses and heavy-duty diesel trucks improved in most cases as emission standards became more stringent. For example, the average NOx emission factors for Euro II, Euro III and Euro IV buses are 11.3±3.3 g km−1, 12.5± 1.3 g km−1, and 11.8±2.0 g km−1, respectively. No statistically significant difference in NOx emission factors was observed between Euro II and III buses. Even for Euro IV buses equipped with SCR systems, the NOx emission factors are similar to Euro III buses. The data regarding real-time engine performance of Euro IV buses suggest the engine certification cycles did not reflect their real-world operating conditions. These new on-road test results indicate that previous estimates of total NOx emissions for HDDV fleet may be significantly underestimated. The new estimate in total NOx emissions for the Beijing HDDV fleet in 2009 is 37.0 Gg, an increase of 45% compared to the previous study. Further, we estimate that the total NOx emissions for the national HDDV fleet in 2009 are approximately 4.0 Tg, higher by 1.0 Tg (equivalent to 18% of total NOx emissions for vehicle fleet in 2009) than that estimated in the official report. This would also result in 4% increase in estimation of national anthropogenic NOx emissions. More effective control measures (such as promotion of CNG buses and a new in-use compliance testing program) are urged to secure the goal of total NOxmitigation for the HDDV fleet in the future
Diesel engine is the most widely used power source of machines. However, faults occur frequently and often cause terrible accidents and serious economic losses. Therefore, diesel engine fault diagnosis is very important. Commonly, a single unitary pattern recognition method is used to diagnose the faults of diesel engine, but its performance decreases sharply when there are many fault types. Targeting this problem, a novel diesel engine fault diagnosis approach is proposed in this study. The approach is composed of four stages. Firstly, the nonstationary and nonlinear vibration signal of diesel engine is decomposed into a series of proper rotation components (PRCs) and a residual signal by the intrinsic time-scale decomposition (ITD) method. Secondly, six typical time-domain and four typical frequency-domain characteristics of the first several PRCs are extracted as fault features. Then, the modular and ensemble concepts are introduced to construct the multistage Adaboost relevance vector machine (RVM) model, in which the kernel fuzzy c-means clustering (KFCM) algorithm is used to decompose a complex classification task into several simple modules, and the Adaboost algorithm is used to improve the performance of each RVM based module. Finally, the fault diagnosis results can be obtained by inputting the fault features into the multistage Adaboost RVM model. The analysis results show that the fault diagnosis approach based on ITD and multistage Adaboost RVM performs effectively for the fault diagnosis of diesel engine, and it is better than the traditional pattern recognition methods.
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