Machinery health prognostics: A systematic review from data acquisition to RUL prediction

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Degradation data analysis and remaining useful life estimation: A review on Wiener-process-based methods

Deep learning-based remaining useful life estimation of bearings using multi-scale feature extraction

Cloud-enabled prognosis for manufacturing

Machine performance degradation assessment and remaining useful life prediction using proportional hazard model and support vector machine

Machine performance degradation assessment and remaining useful life (RUL) prediction are of crucial importance in condition-based maintenance to reduce the maintenance cost and improve the reliability. They provide a potent tool for operators in decision-making by specifying the present machine state and estimating the remaining time. For this ultimate purpose, a three-stage method for assessing the machine health degradation and forecasting the RUL is proposed. In the first stage, only the normal operating condition of machine is used to create identification model for recognizing the dynamic system behavior. Degradation index which is used for indicating the machine degradation is subsequently created based on the root mean square of residual errors. These errors are the difference between identification model and behavior of system. In the second stage, the Cox’s proportional hazard model is generated to estimate the survival function of the system. In the last stage, support vector machine, which is one of the remarkable machine learning techniques, in association with time-series techniques is utilized to forecast the RUL. The data of low methane compressor acquired from condition monitoring routine is used for validating the proposed method. The result shows that the proposed method could be used as a reliable tool to machine prognostics.

This paper deals with dynamic analysis of Pipeline Inspection Gauge (PIG) flow control in natural gas pipelines. The dynamic behaviour of PIG depends on the pressure differential generated by injected gas flow behind the tail of the PIG and expelled gas flow in front of its nose. To analyze dynamic behaviour characteristics (e.g. gas flow, the PIG position and velocity) mathematical models are derived. Two types of nonlinear hyperbolic partial differential equations are developed for unsteady flow analysis of the PIG driving and expelled gas. Also, a non-homogeneous differential equation for dynamic analysis of the PIG is given. The nonlinear equations are solved by method of characteristics (MOC) with a regular rectangular grid under appropriate initial and boundary conditions. Runge-Kutta method is used for solving the steady flow equations to get the initial flow values and for solving the dynamic equation of the PIG. The upstream and downstream regions are divided into a number of elements of equal length. The sampling time and distance are chosen under Courant-Friedrich-Lewy (CFL) restriction. Simulation is performed with a pipeline segment in the Korea gas corporation (KOGAS) low pressure system, Ueijungboo-Sangye line. The simulation results show that the derived mathematical models and the proposed computational scheme are effective for estimating the position and velocity of the PIG with a given operational condition of pipeline.

Modeling and simulation for PIG flow control in natural gas Pipeline

This paper introduces a simple nonlinear control method for pipeline inspection gauge (PIG) flow in a natural gas pipeline. The PIG is controlled using the amount of bypass flow across its body. The dynamic behavior of the PIG depends on the different pressure across its body and the bypass flow through it. The system dynamics includes: dynamics of driving gas flow behind the PIG, dynamics of expelled gas in front of the PIG, dynamics of bypass flow and dynamics of the PIG. The method of characteristics (MOC) and Runge-Kutta method are used to solve the dynamics of flow. To control the PIG velocity, a simple nonlinear controller is proposed based on the back-stepping method. The closed loop system is stable in the sense of Lyapunov stability. To derive such a controller, three system parameters should be measured: the PIG position, its velocity and the velocity of bypass flow across the PIG body. To show the effectiveness of the proposed controller, the simulation has been done with three cases: the PIG starts to move at its launcher, the PIG arrives at its receiver and the PIG restarts after stopping in the pipeline. The simulation results show that the proposed controller can be used for controlling the PIG velocity with good performance when it runs in the natural gas pipeline.

Speed control of PIG using bypass flow in natural gas pipeline

Dynamic modeling and its analysis for the PIG (pipeline inspection gauge) flow through a 90/spl deg/ curved pipe with compressible and unsteady flow are studied. The PIG dynamics model is derived by using a Lagrange equation under the assumption that it passes through 3 different sections in the curved pipeline such that it moves into, inside and out of the curved section. The downstream and up stream flow dynamics including the curved sections are solved using the method of characteristic. The effectiveness of the derived mathematical models is estimated by simulation results for a low pressure natural gas pipeline including downward and upward curved sections. The simulation results show that the proposed model and solution can be used for estimating the PIG dynamics when we pig the pipeline including curved sections.

Tan Tien Nguyen

Papers

Machine performance degradation assessment and remaining useful life prediction using proportional hazard model and support vector machine

Machine performance degradation assessment and remaining useful life prediction using proportional hazard model and support vector machine

Modeling and simulation for PIG flow control in natural gas Pipeline

Speed control of PIG using bypass flow in natural gas pipeline

Dynamic modeling and its analysis for PIG flow through curved section in natural gas pipeline