Simultaneous faults identification of rolling element bearings and gears by combining kurtogram and independent component analysis

A combination of kurtogram and independent component analysis (ICA) is proposed in this paper to identify the faults of rolling element bearings (REBs) and gears existing simultaneously in a gearbox. In the proposed scheme, multi-channel vibrations are picked up from the gearbox at first. Then, the fast kurtogram algorithm is employed to extract the envelopes of each vibration from different channels. Subsequently, the envelopes are separated by an ICA algorithm into independent envelope components according to different sources. Finally, the characteristic frequencies of both the faulty REB and the faulty gear can be exposed simultaneously in the envelope spectral plots. A simulation and an experimental test are introduced to show the effectiveness of the proposed method

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

Optimal Boundary Control of an Axially Moving Material System

[[abstract]]The objective of this paper is to develop an optimal boundary control strategy for the axially moving material system through a mass-damper-spring (MDS) controller at its right-hand-side (RHS) boundary. The partial differential equation (PDE) describing the axially moving material system is combined with an ordinary differential equation (ODE), which describes the MDS. The combination provides the opportunity to suppress the flexible vibration by a control force acting on the MDS. The optimal boundary control laws are designed using the output feedback method and maximum principle theory. The output feedback method only includes the states of displacement and velocity at the RHS boundary, and does not require any model discretization thereby preventing the spillover associated with discrete parameter models. By utilizing the maximum principle theory, the optimal boundary controller is expressed in terms of an adjoint variable, and the determination of the corresponding displacement and velocity is reduced to solving a set of differential equations involving the state variable, as well as the adjoint variable, subject to boundary, initial and terminal conditions. Finally, a finite difference scheme is used to validate the theoretical results

Vision-Based Control of the Mechatronic System

[[abstract]]The mechatronic system is employed widely in the industry, transportation, aviation and military. The system consists of an electrical actuator and a mechanism, and commonly is effective in industry territory. The toggle mechanism has many applications where overcome a large resistance with a small driving force is necessary; for examples, clutches, rock crushers, truck tailgates, vacuum circuit breakers, pneumatic riveters, punching machines, forging machines and injection modeling machines, etc. The motion controls of the motor-toggle mechanism have been studied (Lin et al., 1997; Fung & Yang, 2001; Fung et al., 2001). (Lin et al.1997) proposed a fuzzy logic controller, which was based on the concept of hitting condition without using the complex mathematical model for a motor-mechanism system. The fuzzy neural network controller (Wai et al., 2001; Wai, 2003) was applied to control a motor-toggle servomechanism. The numerical results via the inverse dynamics control and variable structure control (VSC) were compared for an electrohydraulic actuated toggle mechanism (Fung & Yang, 2001). The VSC (Fung et al., 2001) was employed to a toggle mechanism, which was driven by a linear synchronous motor and the joint coulomb friction was considered. In the previous studies, the motion controller for the toggle mechanism had been performed extensively. But the controllers are still difficult to realize if the linear scales can not be installed in the toggle mechanism for real feedbacks of positions and speeds. In the adaptive control territory, (Li et al. 2004) proposed a hybrid control scheme for the flexible structures to obtain both dynamic and static characteristics. A nonlinear strategy is proposed by (Beji & Bestaoui, 2005) to ensure the vehicle control, in which the proof of main results is based on the Lyapunov concept. In these studies, the linear scale or encoder was employed as the sensor to feedback the positions and speeds. If the sensor is difficult to install, the non-contact measure vision-based is necessary and effective to apply in the mechatronic system. In such motor-mechanism coupled systems, the non-contact machine vision exhibits its merits to measure the output responses of the machine. In previous references (Petrovic & Brezak, 2002; Yong et al., 2001), the machine vision was implemented with the PI and PD controllers, but didn’t concern about the robustness of the vision system associated with controllers. (Park & Lee, 2002) presented the visual servo control for a ball on a plate and tracked its desired trajectory by the SMC. But there was no comparison with any other controller, and the mathematical equations of motion must be exactly obtained first, then the SMC can be implemented. (Petrovic & Brezak, 2002) applied the vision systems to motion control, in which the hard real-time constrains was put on image processing and was suitable for real-time angle measurement. In the autonomous vehicle (Yong et al., 2001), the reference lane was continually detected by machine vision system in order to cope with the steering delay and the side-slip of vehicle, and the PI controller was employed for the yaw rate feedback. (Nasisi & Carelli, 2003) designed the adaptive controllers for the robot’s positioning and tracking by use of direct visual feedback with camera-in-hand configurations. In these previous studies, they did not either discuss about the robustness of the vision system associated with the controllers or investigate robustness performances of the controllers for robot systems in experimental realization. The control techniques are essential to provide a stable and robust performance for a wide range of applications, e.g. robot control, process control, etc., and most of the applications are inherently nonlinear. Moreover, there exist relatively little general theories for the adaptive controls (Astrom & Wittenmark, 1995; Slotine & Li, 1991) of nonlinear systems. As the application of a motor-toggle mechanism has similar control problems to the robotic systems, the adaptive control technique developed by (Slotine and Li, 1988, 1989), which exploited the conservation of energy formulation to design control laws for the fixed position control problem, is adopted to control the motor-toggle mechanism in this chapter. The techniques made use of matrix properties of a skew-symmetric system so that the measurements of acceleration signals and the computations of inverse of the inertia matrix are not necessary. Moreover, an inertia-related Lyapunov function containing a quadratic form of a linear combination of speed- and position-error states will be formulated. Furthermore, the SMC, PD-type FLC (Rahbari & Silva, 2000) and PI-type FLC (Aracil & Gordillo, 2004) are proposed to positioning controls, and their performances by machine vision are compared between numerical simulations and experimental experiments. In this chapter, the machine vision system is used as the sensor to measure the output state of the motor-toggle mechanism in real operational conditions. The shape-pattern and color-pattern (Hashimoto & Tomiie, 1999) on the link and slider are applied as the output objects to measure by the machine vision system. The main advantage of a vision-based measuring system is its non-contact measurement principle, which is important in cases when the contact measurements are difficult to implement. In the theoretical analysis, Hamilton’s principle, Lagrange multiplier, geometric constraints and partitioning method are employed to derive the dynamic equations. In order to control the motor-mechanism system with robust characteristics, the SMC is designed to control the slider position. However, the general problem encountered in the design of a SMC system is that the bound of uncertainties and the exact mathematical mode of the motor-mechanism system are difficult to obtain in practical applications. In order to overcome the difficulties, the PI-type FLC, which is based on the concept of hitting conditions and without using the complex mathematical model of the motor-mechanism system, is successfully proposed by machine vision numerically and experimentally. This chapter is organized as follows. After an introduction in Section 1, a mathematical modeling is in Section 2. Section 3 shows the design of the vision-based controller. Section 4 is the numerical simulations. The machine-vision experiments are in Section 5. Finally, experimental results and conclusions are shown in Section 6 and 7, respectively

Trajectory planning based on minimum absolute input energy for an LCD glass-handling robot

[[abstract]]The main idea of this paper is to design a novel point-to-point (PTP) trajectory based on minimum absolute input energy (MAIE) for an LCD glass-handing robot, which is driven by a permanent magnet synchronous motor (PMSM). The mechatronic system is described by a mathematical model of electrical and mechanical coupling equations. To generate the MAIE PTP trajectory, we employ a high-degree polynomial and compare with the trapezoidal, cycloidal and zero-jerk trajectories for various constraint conditions, which satisfy their corresponding desired constraints of angular displacement, speed, acceleration and jerk at the start and end times. The real-coded genetic algorithm (RGA) is used to search for the coefficients of high-degree polynomials for the PTP trajectories, and the inverse of absolute input electrical energy is adopted as a fitness function. From numerical simulations, it is found that either increasing the degree number of polynomials or decreasing the constraints at the start and end times will decrease the absolute input electrical energy. The proposed methodology for designing the MAIE PTP trajectory can also be applied to any mechatronic system driven by a PMSM