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Suppose that the data series of groups can be obtained in a time period: where is sampling time. Because the geometric surface of is pointing up, we can find , that is, being minimal points of. Archived from the original on May 2, Kinney and William G.
24/07/ · «Catastrophe industrielle»: le cinéma demande de l'aide face à la mise en place du pass sanitaire. 24 juil. , - Avec AFP.
La pollution industrielle désigne la part de la pollution de l'environnement directement induite par l'Industrie quand elle introduit des altéragènes biologiques, physiques (dont radiations telles que la radioactivité ou dans la lumière artificielle quand elle perturbe l'environnement nocturne), chimiques ou organiques, affectant de manière plus ou moins importante le fonctionnement de l ...
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30/08/ · Afin d'éviter le spectre d'une catastrophe industrielle à la Areva, Iberdrola devrait méditer l'ancien dicton breton: «on ne fait pas de trou dans le granit», surtout si la dolérite est.
We can obtain. Equilibrium Surface. Bifurcation Set. Where and are the coefficients. For parameters and , the optimal value can be obtained by solving the extrema method with the multivariate function: supposing we collect sets of continuous adjoining data including normal and abnormal data , put them into and , and get cumulative error:.
Our goal is to find the value of for each group ; we can obtain. So we use the sum of squares to find the optimal.
According to above formula and , we can obtain. By solving 10 , we can get all of the extremum sets where is the number of extreme points.
Because the geometric surface of is pointing up, we can find , that is, being minimal points of. From Figure 1 , it is obvious that, on the control parameter plane u-v , if , for each , has 3 values on the surface; that is, the manufacturing system has three kinds of mode that one of three modes is theoretically reachable but practically impossible, which is unreachable.
When , the two kinds of mode merge into a single one. In this case, the manufacturing system will change suddenly from one mode to the others. When , has only one value, which indicates that the manufacturing system is single mode. So is the separatrix between the single mode and the multimode of the manufacturing system, where the modal catastrophe of the manufacturing system occurs.
And we found that the control parameters can only go from region to the edge as catastrophe occurs. In actual operation, the control parameters cannot be reached from the region of to the edge of. So, if the control parameters of the manufacturing system are located in , it shows that the manufacturing system is in a risky state. In this case, the control parameter can easily be changed to the edge and catastrophe occurs.
In contrast, if the control parameters are located in , the manufacturing system will stay in a stable state. So, if the upper lobe represents the normal state of manufacturing system, lower lobe represents fault state, and the above analysis is followed; when , the manufacturing system has two kinds of modes: one of them is in a normal state while the other one is faulty. With the change of control parameters , manufacturing systems keep stable in one state, until , and manufacturing system will suddenly jump from upper lobe to lower lobe and breaks down, or contrary.
Therefore, in order to make the manufacturing system run healthily, we should control and make ; then the purpose of preventive control is achieved. Through the analysis of the mutation model shown in Figure 1 , we can obtain the boundary between normal and abnormal in the manufacturing system.
Then, according to the above definition, we get the following, as shown in Table 2. For manufacturing enterprises, the best thing is to reduce the occurrence of production failures. The control processes can be divided into three parts Figure 2 : 1 measure the actual operation of the manufacturing system according to the logic relationship established in the table; 2 judge whether the manufacturing system will suddenly fall into failure according to the catastrophe model; 3 take management actions to control parameters and prevent the system from falling.
In order to visually show the core ideas of this paper. Let us take , the change of in Figure 2 is analyzed. First, map Figure 1 in the - plane and get the internal mechanism part shown in Figure 2.
The curve and represent the upper branch of counter-S curve in Figure 1 , and indicates the normal state of the manufacturing system, where point 3 is a critical point that the system goes from single mode to multimode. Point 4 is in the bifurcation set and represents the critical point where the manufacturing system jumps from the normal state to the fault state.
The curve represents the central part of counter-S curve in Figure 1 and represents the state that the manufacturing system, in the actual operation, cannot reach, where point 2 is in the bifurcation set and represents the critical point at which the manufacturing system jumps from the fault state to normal state. The curves and represent the lower branch of counter-S curve in Figure 1 , and it indicates the fault state of the manufacturing system.
The curve indicates that although the manufacturing system is faulty, it can be restored to the normal state by changing the values of and , but curve indicates the manufacturing system cannot recover itself and can only be repaired manually. Analyzing Figure 2 , when , the manufacturing system state variable is located in x o1 of upper branch, and when v is increased from v 1 to v 4 , the state variable x changes to x o4.
At this time, if we added an infinitesimal perturbation to v 4 , the state of variable x will jump from x o4 to x o5. When v reduces to v 2 , the state of variable x changes to x o2.
At this time, as long as v 2 gets a little bit smaller, the state of variable x will jump from x o2 to x o3 in point 2 and the state of variable x enters upper branch of counter-S curve. Therefore, according to the above analysis, we can control the failure of manufacturing system. The specific process is shown in Figure 2 : firstly, the operation data of the manufacturing system are collected and analyzed and, then, according to Table 2 , the data will be displayed in real-time by bars in different colors.
By this way, we can find the operational problem in manufacturing systems, and then it is controlled according to the internal mechanism of fault in the manufacturing system, to keep the system running in a healthy state. A simplified proof-of-concept case is illustrated to show the process of the proposed method.
Yonggu is a company that produces metal tools and has a complete IoT Internet of things system in its workshop. We use the IoT system to collect real-time data of the product workshop and then select the adjacent data, including normal data and abnormal data, to establish the catastrophe model.
In this section, the throughput and the production load of the manufacturing system within the duration time are selected as the monitoring parameters. In addition, it should be noted that according to the parameter estimation requirements of the catastrophe model, the data must be extracted in an interval; that is, the data in an interval must be continuous in time. Suppose that the data series of groups can be obtained in a time period: where is sampling time.
In this paper, the method of clustering is used to distinguish the normal and abnormal data in the original data sequence. This method differentiates normal data and abnormal data based on the similarity of data between data based on the distance between data points , and the effect of isolation or noise points on classification is erased using this method.
Therefore, after the sampling data being preprocessed, the adjacent normal data and abnormal data of the state variable are obtained. Some sample data is shown in Figure 4. Part experimental data.
In each interval, the data was collected continuously and composed of abnormal data and normal data. Now we use the above data to set up catastrophe model. First, to satisfy the above equation of catastrophe flow and bifurcation set, the following equation is satisfied:. And then, according to above formula and , we can obtain. By analyzing the above model, the computational complexity is. Using above data and references [ 26 ], 10 solutions were obtained, by using.
And then the optimal values and can be obtained. Next, the other intervals data can be used to modify the parameters of the model, finally we obtain and.
The graph of the equilibrium surface and the bifurcation set is represented in Figure 5. Let the system state be represented by in the three-dimensional space; then the phase point must be located on the surface and always on the upper lobe or lower lobe of the surface. In Figure 5 , the upper lobe indicates the normal state of manufacturing system, the lower lobe indicates the fault state, and the folding part indicates an unreachable state of the manufacturing system.
The projection of the equilibrium hypersurfaces in the control plane , namely, u-v , is a topological transformation or mapping, which can be represented by. As shown in Figure 6 , the first figure in Figure 6 corresponds to the path 1 in Figure 5. When the control parameters are not controlled at , then will fall on the curve of at the next moment, and the manufacturing system will suddenly break down.
At this time, the manufacturing system is still able to operate, and though certain adjustments can be restored to normal operation, if the is still not controlled, will enter , and the system cannot work and manual repair is required.
The second figure in Figure 6 is the result of controlling before system fault, corresponding to path 2 in Figure 5. From path 2 in Figure 5 , we can know that preventing from at point 1; we can change the evolutionary path of the system and prevent the system from breaking down suddenly. The third figure in Figure 6 is the result of controlling after system fault, corresponding to path 3 in Figure 5. From path 3 in Figure 5 , we can know that changing the values of and , satisfying at point 2; we can bring the manufacturing system back to normal suddenly.
Data-driven methods such as support vector machine SVM , PCA, and spectrum analysis are based on a large number of real-time data for training, so it has requirements on the quantity of data. However, in this paper, the fault data is small sample, so data-driven methods cannot be able to adopt in this paper. In the following part, the value-driven method is used to predict and analyze manufacturing system faults.
Now, we establish 4 layers networks as show in Figure 7 : the input layers have 2 neurons, the first hidden layers have 12 neurons, the second hidden layers have 6 neurons, and the output layer have 1 neuron. The elastic conjugate gradient descent method with momentum is used for network training by groups of part experimental data in Figure 4. In order to be consistent with the events in Figure 6 , we predict and analyze the next 10 events.
However, by Figure 9 , we can know deep learning has poor prediction effect in such case. The reason is that the fault data is a small sample, so the deep learning method is difficult to achieve good prediction effect.
The combination method of analysis model and data driven proposed in this paper can effectively make up the drawback that data-driven and knowledge-driven have too high requirements for fault data volume. Previous fault analysis based on data driven and value driven can predict the type of fault and the time of fault occurrence accurately; however, it is a big issue that the cause of fault and evolution mechanism cannot be found.
Therefore, the evolution mechanism of fault is of great significance to the management and operation of an enterprise.
In this paper, the catastrophe model of manufacturing system fault is established and then by solving and analyzing model, it is found that 1 If the control variables , the manufacturing system will stay in the normal state. However, with the change of , the manufacturing system will break down suddenly on.
Through the above analysis, the internal mechanism of fault evolution is found and the preventive control of fault is realized. In this paper, the cusp catastrophic model was proposed to describe manufacturing system fault and to explain fault evolution mechanism in a production process.
First, the operation state of the manufacturing system is described with two internal and external macro order parameters. The external macro order parameters are taken as state variables and the internal macro order parameters as control variables and.
In the process of solving model parameters and , k-mean clustering algorithm is used for data preprocessing, and then the extremum of multivariate functions is used for the optimal parameters and. Finally, the dynamics method is used to analyze the cusp catastrophe model, to find out the internal mechanism of fault evolution in the manufacturing system and to design the logic operation according to the internal mechanism of evolution, so as to realize the real-time monitoring and preventive control of the manufacturing system.
The experimental data in Figure 4 used to support the findings of this study are belongs to Yonggu bloc in zhejiang province. Hence, we just provide part data that are included within the supplementary information file s available here. Some experimental data are given in the attachment; three columns of data are represented, respectively, duration, production load, and throughput.
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La prévention des risques industriels : un état des lieux ...
15/05/2020 · Au niveau européen, la directive Seveso, nom d’une commune italienne victime d’une catastrophe industrielle en 1976, a mis en place des obligations pour les établissements à risques. Elles varient selon deux catégories : seuil haut et seuil bas, en fonction des quantités de substances dangereuses susceptibles d’être présentes sur ...
30/08/ · Afin d'éviter le spectre d'une catastrophe industrielle à la Areva, Iberdrola devrait méditer l'ancien dicton breton: «on ne fait pas de trou dans le granit», surtout si la dolérite est. 05/08/ · Catastrophe: A Guide to World's Worst Industrial Disasters. Catastrophe.: Terra Pitta. Vij Books India Pvt Ltd, Aug 5, - Social Science - 1 Review. Disasters have been a menace, throughout history. Earlier, disasters were, mainly due to natural happenings and unfortunate incidents, like epidemics, droughts, earthquakes 1/5(1). AZF: une catastrophe industrielle majeure, suivie d'une longue bataille judiciaire. Il y a vingt ans, l'explosion de l'usine chimique AZF à Toulouse faisait 31 morts et des milliers de blessés.
La prévention des risques : directive Seveso et loi Risques
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