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The continuous miner technology (CMT) is being adopted extensively in underground coal mines of India as mass production technology. This technology potentially eliminates the two-unit operating, namely the drilling and blasting. This elimination helps for better strata control and avoids the drillers working in unsafe conditions, maybe under the loose coal roof. This CMT improves the quality of the coal extracted and increases the output per man shift by reducing the deployment of face crew. It also reduces the accident as it is operated by remote and workforce exposure to faces is limited. The method also helps for better roof and side control, thus preventing side and roof fall danger. With the changing time now, continuous miners of different heights are available, which helps for optimum use of technology to mine out varying in situ height of coal seam. This paper discusses the performance of the CMT in the Sarpi mine and compares the technology available globally.

In recent times, intelligent predictive systems are showing greater levels of accuracy and effectiveness in early detection of the critical diseases of cancer in the liver, lungs, etc. Predictive models assist medical practitioners to identify the diseases based on symptoms and health indicators like hormones, enzymes, age, blood counts, etc. This article focuses on proposing an optimal classification model to detect chronic liver disease by enhancing the prediction accuracy through cutting-edge analytics. The article proposes an enhanced framework on the original study by Ramana et al. It uses measures like precision and balanced accuracy to choose the most efficient classification algorithm in Indian and USA patient datasets using various factors like enzymes, age, etc. Using Youden's index, individual thresholds for each model were identified to increase the power of sensitivity and specificity, respectively. The study proposes a framework for highly accurate automated disease detection in the medical industry and helps in strategizing preventive measures for patients.

Intelligent predictive systems are showing a greater level of accuracy and effectiveness in early detection of critical diseases like cancer and liver and lung disease.Predictive models assist medical practitioners in identifying the diseases based on symptoms and health indicators like hormone,enzymes,age,bloodcounts,etc.This study proposes a framework to use classification models to accurately detect chronic liver disease by enhancing the prediction accuracy through cutting-edge analytics techniques.The article proposes an enhanced framework on the original study by Ramana et al. (2011).It uses evaluation measures like Precision and Balanced Accuracy to choose the most efficient classification algorithm in INDIA and USA patient datasets using various factors like enzymes,age,etc.Using Youden’s Index, individual thresholds for each model were identified to increase the power of sensitivity and specificity.A framework is proposed for highly accurate automated disease detection in the medical industry,and it helps in strategizing preventive measures for patients with liver diseases.

The state-of-the-art technology of continuous mining technology by deploying continuous miners (CM) is a unique method to extract the coal seams lying at greater depth, thereby minimising the loss of coal in terms of in situ. Continuous miner technology (CMT) is a proven and affordable technology in terms of investment. The continuous miner with the fastest extraction invites rapid load distribution in the overlying strata. The rib and snook’s irregular geometry makes it quite tricky to calculate the load over the snook by the empirical formulae. As the straight line of the extraction is being followed in the depillaring panel, snook is a vital input to control the periodic caving and safe operation. To determine the proper snook’s dimensions, it is essential to know all the factors influencing the failure, whether direct or indirect. However, for the successful and safe operation of the continuous miner, the proper design and calculation of the exact load in the snook are essential. Few accidents occurred due to rib and snook failure, which resulted in a relaxed attitude towards the calculation. In this research paper, estimation of the load in the snook is discussed by numerical modelling in the FLAC3D software, which is further validated by field instruments. With the help of numerical modelling factor of safety was determined and compared with the existing actual snook of the Jhanjra mine. The average stress is thus calculated by the different sizes and the position of every snook. The results presented in this research can be used as a guideline for the successful operation of pillar extraction by CMT.

Model-based fault detection and isolation (FDI) needs an analytical model from which residuals can be obtained from a definite cause and effect relationships. The residuals are considered as the fault indicators, which are utilized for root cause analysis via fault signature and parameter estimation. After the disambiguation of fault, the job remains to take a decision in the supervision platform whether the fault can be accommodated (or tolerated) with the existing instrumentation. This applicability of fault tolerant control (FTC) depends upon actuator sizing through system inversion and control law design. In this work, a proven methodology for FDI using bond graph modeling has been applied to an oil hydraulic test bench for isolating a faulty component. The test bench was designed with a provision to inject a fault. After isolation of the faulty component, estimation of faulty parameters, inversion of systems, actuator sizing, and FTC have been addressed. In this paper, a novel fault tolerant control (FTC) using system inversion with due consideration of actuator constraints is reported. In addition, the receding horizon control as FTC has been corroborated through experimentation in a hydraulic drive, where two-step PI control is used to comply with actuator constraints.

In this article, a least square optimization method for multi-fault detection and isolation has been revisited and validated through simulation and experimentation on a pedagogical hydrostatic transmission system. A nonlinear regression analysis has been made on the state equations, obtained from bond graph model of the system, to estimate the unknown parameters as a part of system identification. The model, assigned with the estimated and some known parameters, was then validated with the responses from the test rig. The rig was designed to impose faults (one at a time and/or simultaneously) in different components for the purpose of experimental validation of multi-fault detection and isolation. The model-based fault isolation was done using structural analysis of some constraint relations called analytical redundancy relations, the numerical evaluation of which is residuals. The robustness in fault isolation was addressed through linear fractional transformation approach to ensure residuals bounded within adaptive threshold under no fault situation. Finally, the isolated faulty parameters were estimated through particle swarm optimization algorithm for fault sizing. This article is directed towards corroboration of the existing fault isolation methodologies through experimentation on a power hydraulic circuit.

This article addresses a robust fault detection and isolation procedure which is applicable for a hybrid system undergoing discrete mode changes. Global analytical redundancy relations, which may undergo change in their structure depending on the switched mode, are derived from the diagnostic hybrid bond graph model of the system. For fault detection and isolation analysis, the residuals, evaluated at every instant from the global analytical redundancy relations, are tested against adaptive thresholds which are changed with mode shift and variation of states, but not changed due to any fault. The parametric uncertainties are included in the diagnostic hybrid bond graph through the linear fractional transformation approach and the adaptive thresholds are obtained from the modified diagnostic hybrid bond graph model. The residuals are made robust in a sense that these would always remain within the upper and lower threshold bound for any perturbation except fault occurrence. For application of the robust fault detection and isolation analysis, a hybrid thermo-fluid system is selected whose behavioural evolution combines both discrete as well as continuous changes of states. It is shown through simulation that with parameter uncertainties in a hybrid system subject to frequent mode shift, robust fault detection and isolation is successfully achieved using the passive approach. The novelty of this work lies in the integration of the model-based diagnosis principle for hybrid systems with the approach for robust residual threshold generation for systems having uncertain parameters.