A non-contact method for part-based process performance monitoring in end milling operations

Surface response to excitation (SuRE) method was originally developed for structural health monitoring (SHM) applications. SuRE was used to evaluate the performance of completed milling operations. The method generates surface waves on the plate and studies the spectrum changes at selected points to detect defects and change of compressive forces. In this study, the length, depth, and width of a slot were changed step by step. The surface of the aluminum plate was excited in the 20–400 kHz range with a piezoelectric element. A laser scanning vibrometer was used to monitor the vibrations at the predetermined grid points after the dimensions of the slot were changed methodically. The frequency spectrums of measured vibrations were calculated by using the Fast Fourier Transformation (FFT). The sums of the squares of the differences (SSD) of the spectrums were calculated to evaluate the change of the spectrums. The SuRE method was able to determine if the dimensions were changed in each case at all the selected points. The scanning laser vibrometer is not feasible to be used at the shop floor. However, the study demonstrated that a piezoelectric element attached to any of the grid points would be able to evaluate the completed machining process.     

Evaluation of driver drowsiness using respiration analysis by thermal imaging on a driving simulator

In this paper, a new non-intrusive driver drowsiness detection method is introduced based on respiration analysis using facial thermal imaging. Drowsiness is the cause of many driving accidents all over the world. Drivers’ respiration system undergoes significant changes from wakefulness to drowsiness and can be used to detect drowsiness. Current respiration measurement methods are intrusive and uncomfortable making respiration the least measured vital sign during driving. In this paper, a new method is presented based on facial thermal imaging to analyze drivers’ respiration signal non-intrusively. Thirty subjects are tested in a car simulator. They are fully awake at the beginning and experience drowsiness during the tests. The mean and the standard deviation of the respiration rate and the inspiration-to-expiration time ratio are extracted from the subjects’ respiration signal. To detect drowsiness, the Support Vector Machine (SVM) and the K-Nearest Neighbor (KNN) classifiers are used. The Observer Rating of Drowsiness method is used for scoring the drowsiness level and validating the proposed method. The performance and the results of both methods are presented and compared. The results indicate that drowsiness can be detected with the accuracy of 90%, sensitivity of 92%, specificity of 85%, and precision of 91%.

     

A Novel Nonlinear Acoustic Health Monitoring Approach for Detecting Loose Bolts

To date, sensors have been the inevitable component of structural health monitoring (SHM) systems. Typically, sensory signals are digitized, processed by computers, and then the information is presented to the operator with plots or warnings depending on the sophistication of the system. This study proposes a novel nonlinear acoustic health monitoring (NAHM) approach for detection of loose bolts, which can work with and without any sensors. The structure is excited with bitonal excitations, which their difference is in the audible range. When the bolts are well tightened, the structure remains silent. But, the structure creates audible sound or verbal warnings in the presence of one or more loose bolts. There is no need for sensor(s), A/D converters or computers between the operator and the structure. However, it is also possible to attach a piezoelectric sensor or to use a microphone/sound level meter for further analysis of the structure’s response. The feasibility of the concept was demonstrated by detecting the loose bolt in a bolted plate system. For demonstrating the industrial potential of the proposed NAHM system, the concept was implemented for two simple washers held with nuts and bolts. Additionally, the intensities of the audible alarms were studied at different torque levels. The proposed NAHM may be used as a low-cost sensor-free SHM or as a backup for conventional nonlinear SHM systems.     

Robust adaptive Lyapunov‐based control of hepatitis B infection

A new robust adaptive controller is developed for the control of the hepatitis B virus (HBV) infection inside the body. The non‐linear HBV model has three state variables: uninfected cells, infected cells and free viruses. A control law is designed for the antiviral therapy such that the volume of infected cells and the volume of free viruses are decreased to their desired values which are zero. One control input represents the efficiency of drug therapy in inhibiting viral production and the other control input represents the efficiency of drug therapy in blocking new infection. The proposed controller ensures the stability and robust performance in the presence of parametric and non‐parametric uncertainties (and/or bounded disturbances). The global stability and tracking convergence of the process are investigated by employing the Lyapunov theorem. The performance of the proposed controller is evaluated using simulations by considering different levels of uncertainties. Based on the obtained results, the proposed strategy can achieve its desired objectives with different cases of uncertainties.Inspec keywords: medical control systems, drugs, drug delivery systems, cellular biophysics, microorganisms, diseases, Lyapunov methods, adaptive controlOther keywords: robust adaptive Lyapunov‐based control, robust adaptive controller, hepatitis B virus infection, uninfected cells, free viruses, antiviral therapy, drug therapy, viral production, global stability, tracking convergence