Optimization of replacement policy for a one-component system subject to Poisson shocks

In reliability engineering, system failures may occur due to intrinsic or extrinsic factors. For example, drinking water systems may fail due to ageing and deterioration (i.e., intrinsic factors) or flooding (i.e., extrinsic factors). An interesting question is: for such systems, how should preventive maintenance be scheduled? This paper investigates this question. The paper develops a maintenance policy for repairable systems subject to extrinsic shocks. It assumes that a system may fail due to either intrinsic factors or extrinsic factors. Reliability indexes and the expected long run cost rate are then derived. A numerical example is given to illustrate the theoretical results

Measurement of solid–liquid mixing quality by using a uniform design method based on image analysis

Solid–liquid mixing has been a common industrial process operation. The measurement of solid–liquid mixing quality can help improve the efficiency of related industrial processes, but there is still a lack of an intuitive, accurate, and simple measurement method. As an important indicator to evaluate the solid–liquid mixing quality, the degree of solid suspension and the uniformity of solid distribution are directly related to mass transfer and reaction efficiency. Therefore, it is necessary to study the solid suspension and distribution in a solid–liquid system. In this work, the solid suspension and distribution of a solid–liquid system composed of glass beads–water stirred by the impeller are studied experimentally via digital image processing combined with statistical analysis. Specifically, images of solid–liquid mixing are first obtained using a camera and digitally processed. The area ratio of the solid in the image is proposed to reflect the degree of solid suspension, and the modified L2-star discrepancy (MD) is then used to quantify the uniformity of the solid distribution. Then, the solid–liquid mixing quality can be characterized by combining the area ratio and solid distribution. The feasibility of this method was proved by qualitative analysis of the solid–liquid mixing state and comparison with known studies. In addition, the effects of various stirring factors on the solid distribution were studied and discussed by using the proposed method. The results show that the method proposed in this paper can measure the quality of the solid–liquid mixing state more directly and is effective and accurate. Furthermore, it was used to find the best experimental parameters in this work. This method is also simpler and cheaper than many other methods. It is of great significance to improve the efficiency of chemical and metallurgical and other industrial processes

Linking component importance to optimisation of preventive maintenance policy

In reliability engineering, time on performing preventive maintenance (PM) on a component in a system may affect system availability if system operation needs stopping for PM. To avoid such an availability reduction, one may adopt the following method: if a component fails, PM is carried out on a number of the other components while the failed component is being repaired. This ensures PM does not take system’s operating time. However, this raises a question: Which components should be selected for PM? This paper introduces an importance measure, called Component Maintenance Priority (CMP), which is used to select components for PM. The paper then compares the CMP with other importance measures and studies the properties of the CMP. Numerical examples are given to show the validity of the CMP

Reliability analysis of two-unit cold standby repairable systems under Poisson shock.

This paper analyses the reliability of a cold standby system consisting of two re- pairable units, a switch and a repairman. At any time, one of the two units is operating while the other is on cold standby. The repairman may not always at the job site, or take vacation. We assume that shocks can attack the operating unit. The arrival times of the shocks follow a homogeneous Poisson process and their magni- tude is a random variable following a known distribution. Time on repairing a failed unit and the length of repairman's vacation follow general continuous probability distributions, respectively. The paper derives a number of reliability indices: sys- tem reliability, mean time to first failure, steady-state availability, and steady-state failure frequency

Statistical Image Analysis on Liquid-Liquid Mixing Uniformity of Micro-Scale Pipeline with Chaotic Structure

The aim of this work is to introduce a novel statistical technique for quantifying the concentration field uniformity of the liquid-liquid mixing process within a micro-scale chaotic pipeline. For illustration, the microscale liquid-liquid mixer in which the inlet direction is parallel to the mixing unit is designed by using the chaotic pipeline with Baker map. Meanwhile, the non-uniformity coefficient method is adopted quantificationally instead of qualitatively estimating the concentration field uniformity of the chaotic micromixer based on uniform design theory and image analysis. Results show that the concentration distribution of the chaotic mixing process of liquid-liquid under various working conditions is obtained by solving the steady-state Navier–Stokes and diffusion convection equations. The average contribution ratio of the three basic mixing units of the chaotic Baker pipeline to the concentration field uniformity is approximately 6:3:1, which is calculated aligned with the fluid flow direction successively. The optimal mixing uniformity can be obtained as the initial velocity is 0.05 m/s and the diffusion coefficient is 5 × 10−9 m2/s, respectively. The reliability of the new method for estimating the concentration field uniformity parameters is explained from three dimensions. The statistical image analysis technique is illustrated to be reliable and effective in yielding accurate concentration field information of the simulated chaotic mixer. Furthermore, it can be adapted to examine a variety of concentration distribution issues in which concentrations are evaluated under distinct scales

Regional Scale Inversion of Chlorophyll Content of <i>Dendrocalamus giganteus</i> by Multi-Source Remote Sensing

The spectrophotometer method is costly, time-consuming, laborious, and destructive to the plant. Samples will be lost during the transportation process, and the method can only obtain sample point data. This poses a challenge to the estimation of chlorophyll content at the regional level. In this study, in order to improve the estimation accuracy, a new method of collaborative inversion of chlorophyll using Landsat 8 and Global Ecosystem Dynamics Investigation (GEDI) is proposed. Specifically, the chlorophyll content data set is combined with the preprocessed two remote-sensing (RS) factors to construct three regression models using a support vector machine (SVM), BP neural network (BP) and random forest (RF), and the better model is selected for inversion. In addition, the ordinary Kriging (OK) method is used to interpolate the GEDI point attribute data into the surface attribute data for modeling. The results showed the following: (1) The chlorophyll model of a single plant was y = 0.1373x1.7654. (2) The optimal semi-variance function models of pai, pgap_theta and pgap_theta_a3 are exponential models. (3) The top three correlations between the two RS data and the chlorophyll content were B2_3_SM, B2_3_HO, B2_5_EN and pai, pgap_theta, pgap_theta_a3. (4) The combination of the Landsat 8 imagery and GEDI resulted in the highest modeling accuracy, and RF had the best performance, with R2, RMSE and P values of 0.94, 0.18 g/m2 and 83.32%, respectively. This study shows that it is reliable to use Landsat 8 images and GEDI to retrieve the chlorophyll content of Dendrocalamus giganteus (D. giganteus), revealing the potential of multi-source RS data in the inversion of forest ecological parameters

The gut-joint axis mediates the TNF-induced RA process and PBMT therapeutic effects through the metabolites of gut microbiota

ABSTRACTThe gut-joint axis, one of the mechanisms that mediates the onset and progression of joint and related diseases through gut microbiota, and shows the potential as therapeutic target. A variety of drugs exert therapeutic effects on rheumatoid arthritis (RA) through the gut-joint axis. However, the anti-inflammatory and immunomodulatory effect of novel photobiomodulatory therapy (PBMT) on RA need further validation and the involvement of gut-joint axis in this process remains unknown. The present study demonstrated the beneficial effects of PBMT on RA, where we found the restoration of gut microbiota homeostasis, and the related key pathways and metabolites after PBMT. We also discovered that the therapeutic effects of PBMT on RA mainly through the gut-joint axis, in which the amino acid metabolites (Alanine and N-acetyl aspartate) play the key role and rely on the activity of metabolic enzymes in the target organs. Together, the results prove that the metabolites of amino acid from gut microbiota mediate the regulation effect on the gut-joint axis and the therapeutic effect on rheumatoid arthritis of PBMT