Problem In Industrial Systems Research Articles

A Hierarchical Quality-Related Fault Diagnosis Method for Nonlinear Industrial Systems

This paper focuses on the quality-related fault diagnosis problem in industrial systems in terms of the proposed hierarchical data-driven (HDD) method. As opposed to the existing approaches, the proposed approach makes use of the ensemble learning method to avoid selecting the optimal kernel parameter, saving computation costs in the training process. Further, a novel quality-related fault diagnosis scheme that incorporates the quality-related fault detector and the weighted fault reconstitution method is designed to monitor the fault and reveal the trends of the quality state. In the end, both a simulation case and an industrial case are applied to demonstrate the effectiveness of the proposed method

Water-Induced Corrosion Damage of Carbon Steel in Sulfolane

Sulfolane in contact with water and oxygen forms acidic (by-) products that are major factors in accelerating the corrosion of carbon/stainless steel. In consequence, water-induced corrosion damage can be a serious problem in industrial systems. Hence, the determination of the corrosion resistance of AISI 1010 steel immersed in sulfolane containing 0 to 6 vol.% water was the principal objective of the study. Evaluation of the corrosion resistance of steel electrodes was performed using a potentiodynamic technique and scanning Kelvin probe microscopy. It was observed that the corrosion products layer that formed on the surface of AISI 1010 steel partially protects it against corrosion in sulfolane with a water concentration in the range from 1 vol.% to 4 vol.%. Interestingly, amounts of water above 4 vol.% cause a break-down of the corrosion products layer and deteriorate the corrosion resistance of AISI 1010 steel as well. Moreover, the relationship between the fractal dimension, corrosion degree of the steel surface and water concentration in sulfolane was investigated. The fractal dimension was determined using 2D grayscale images of AISI 1010 steel registered through a scanning electron microscope. It was noticed that both the fractal dimension and the corrosion degree rose with the increased water concentration in sulfolane.

Sustainability of the water footprint of the Spanish pork industry

Around 92% of the humanity's footprint (WF) relates to the agricultural sector, and a considerable proportion of this is associated with animal farming. In Spain, the swine sector accounts for 11% of agricultural output in economic terms and makes substantial demands on freshwater resources. In this study we estimate the WF of the Spanish pig sector at an average 19.5 billion m3/yr (82% green, 8% blue, 10% grey) over the period 2001–08. During this period the WF increased by 23%, due to growing exports. About half the water needed to produce concentrate feed comes from Spain, with the remaining 50% embodied in imported feedstock products. When comparing the blue and grey WFs of feed production in the source regions with indicators of water scarcity and water pollution, we find that most of the feed produced in Spain, unlike that imported, comes from watersheds where freshwater resources are overexploited. The evaluation of the WF of four different pig production systems shows that pigs raised in extensive systems have the largest WF per tonne of live animal. However, water pollution is a particular problem in industrial systems given the high geographical concentration of animals. The swine sector is one of the largest consumers of natural resources in Spain and should, therefore, be an important focal point in agricultural, environmental and water policies.

A Comparative Study of Firefly Algorithm and Shuffled Frog-Leaping Algorithm for Constrained Free and Manufacturing Optimization Problems

Manufacturing process problems in industrial systems are currently large and complicated. The effective methods for solving these problems using a finite sequence of instructions can be classified into two groups; optimization and meta-heuristic algorithms. In this paper, a well-known meta-heuristic approach called Firefly Algorithm was used to compare with Shuffled Frog-leaping Algorithm. All algorithms were implemented and analyzed with manufacturing process problems under different conditions, which consist of single, multi-peak and curved ridge optimization. The results from both methods revealed that Firefly Algorithm seemed to be better in terms of the mean and variance of process yields including design points to achieve the final solution.

Particle Filtering for State Estimation in Nonlinear Industrial Systems

State estimation is a major problem in industrial systems, particularly in industrial robotics. To this end, Gaussian and nonparametric filters have been developed. In this paper, the extended Kalman filter, which assumes Gaussian measurement noise, is compared with the particle filter, which does not make any assumption on the measurement noise distribution. As a case study, the estimation of the state vector of an industrial robot is used when measurements are available from an accelerometer that was mounted on the end effector of the robotic manipulator and from the encoders of the joints' motors. It is shown that, in this kind of sensor fusion problem, the particle filter outperforms the extended Kalman filter, at the cost of more demanding computations.

Particle and Kalman filtering for state estimation and control of DC motors

State estimation is a major problem in industrial systems. To this end, Gaussian and nonparametric filters have been developed. In this paper the Kalman Filter, which assumes Gaussian measurement noise, is compared to the Particle Filter, which does not make any assumption on the measurement noise distribution. As a case study the estimation of the state vector of a DC motor is used. The reconstructed state vector is used in a feedback control loop to generate the control input of the DC motor. In simulation tests it was observed that for a large number of particles the Particle Filter could succeed in accurately estimating the motor’s state vector, but at the same time it required higher computational effort.

Particle filtering for state estimation in industrial robotic systems

State estimation is a major problem in industrial systems. To this end, Gaussian and non-parametric filters have been developed. In this paper the extended Kalman filter which assumes Gaussian measurement noise is compared to the particle filter which does not make any assumption on the measurement noise distribution. As a case study the estimation of the state vector of an industrial robot is used, when measurements are available from an accelerometer mounted on the end-effector of the robotic manipulator and from the encoders of the joint motors. It is shown that in this kind of sensor fusion problem the particle filter outperforms the extended Kalman filter, at the cost of more demanding computations.

Monitoring Microbial Fouling and Corrosion Problems in Industrial Systems

Monitoring Microbial Fouling and Corrosion Problems in Industrial Systems