In the era of Industry 4.0, the ease of access to precise measurements in real-time and the existence of machine-learning (ML) techniques will play a vital role in building practical tools to isolate inefficiencies in energy-intensive processes. This paper aims at developing an abnormal event diagnosis (AED) tool based on ML techniques for monitoring the operation of industrial processes. This tool makes it easier for operators to accomplish their tasks and to make quick and accurate decisions to ensure highly efficient processes. One of the most popular ML techniques for AED is the multivariate statistical control (MSC) method; it only requires the dataset of the normal operating conditions (NOC) to detect and identify the variables that contribute to abnormal events (AEs). Despite the popularity of MSC, it is challenging to select the appropriate method for detecting and isolating all possible abnormalities a complex industrial process can experience. To address this limitation and improve efficiency, we have developed a generic methodology that integrates different ML techniques into a unified multiagent based approach, the selected ML techniques are supposed to be built using only the normal operating condition. For the sake of demonstration, we chose a combination of two ML methods: principal component analysis and k-nearest neighbors (k-NN). The k-NN was integrated into the proposed multiagent to take into account the nonlinearity and multimodality that frequently occur in industrial processes. In addition, we modified a k-NN method proposed in the literature to reduce computation time during real-time detection and isolation. Finally, the proposed methodology was successfully validated to monitor the energy efficiency of a reboiler located in a thermomechanical pulp mill.
相似文献Background
Travel practices are changing: bicycle and motorized two-wheeler (MTW) use are rising in some of France’s large cities. These are cheaper modes of transport and therefore attractive at a time of economic crisis, but they also allow their users to avoid traffic congestion. At the same time, active transport modes such as walking and cycling are encouraged because they are beneficial to health and reduce pollution. It is therefore important to find out more about the road crash risks of the different modes of transport. To do this, we need to take account of the number of individuals who use each, and, even better, their travel levels.Method
We estimated the exposure-based fatality rates for road traffic crashes in France, on the basis of the ratio between the number of fatalities and exposure to road accident risk. Fatality data were obtained from the French national police database of road traffic casualties in the period 2007–2008. Exposure data was estimated from the latest national household travel survey (ENTD) which was conducted from April 2007 to April 2008. Three quantities of travel were computed for each mode of transport: (1) the number of trips, (2) the distance traveled and (3) the time spent traveling. Annual fatality rates were assessed by road user type, age and sex.Results
The overall annual fatality rates were 6.3 per 100 million trips, 5.8 per billion kilometers traveled and 0.20 per million hours spent traveling. The fatality rates differed according to road user type, age and sex. The risk of being killed was 20 to 32 times higher for motorized two-wheeler users than for car occupants. For cyclists, the risk of being killed, both on the basis of time spent traveling and the number of trips was about 1.5 times higher than for car occupants. Risk for pedestrians compared to car occupants was similar according to time spent traveling, lower according to the number of trips and higher according to the distance traveled. People from the 17–20 and 21–29 age groups and those aged 70 and over had the highest rates. Males had higher rates than females, by a factor of between 2 and 3.Conclusion
When exposure is taken into account, the risks for motorized two-wheeler users are extremely high compared to other types of road user. This disparity can be explained by the combination of speed and a lack of protection (except for helmets). The differential is so great that prevention measures could probably not eliminate it. The question that arises is as follows: with regard to public health, should not the use of MTW, or at least of motorcycles, be deterred? The difference between the fatality risk of cyclists and of car occupants is much smaller (1.5 times higher); besides, there is much room for improvements in cyclist safety, for instance by increasing the use of helmets and conspicuity equipment. Traffic calming could also benefit cyclists, pedestrians and perhaps moped users. 相似文献The wind energy conversion system model developed for the design and evaluation of the proposed fault detection technique including three principal controls. the first control ensure the regulation of the electromagnetic torque and the reactive stator power (named Rotor Side Converter (RSC) control), the second regulates the DC-link voltage at the desired level (named Grid Side Converter (GSC) control) and in order to achieve maximum power at any wind speed condition a maximum power point tracking (MPPT) control strategy has been used. The simulation model was developed in MATLAB/Simulink environment. The results show that the proposed fault detection scheme is able to rapidly and effectively identify open switch faults among other fault types in a time less than one period. 相似文献