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Screen time lets you know how much time you and your kids spend on apps, websites, and more. Parental controls aren’t perfect, and in fact, there’s a lot of undesirable content that can slip through parental controls. It also helps to keep your kids safe online and to provide themselves with some peace of mind. SET UP TIMECONTROL DASHBOARD FREEThese free and premium apps will help you limit and take control of your daily screen time. SET UP TIMECONTROL DASHBOARD ANDROIDThe set of gears provides a 278:1 reduction going from a motor speed of about 6117 rpm to about 22 rpm at the output shaft when the DC motor is driven at 5 volts.įor more information about the hardware model, see Live RUL Estimation of a Servo Gear Train Using ThingSpeak.Thinking of installing a screen time app on your iPhone or Android devices or on your kid’s phone? You’re not alone. The stepped gear sets G1 and P2, G2 and P3, and G3 and P4 are free spinning gears − that is, they are not fixed to their respective shafts. Pinion P4, which is molded with G3, meshes with the final gear G4 that is attached to the output shaft. The pinion P3, which is a molded part of gear G2, meshes with the stepped gear G3. The pinion P2 is a molded part of the stepped gear G1 and meshes with the stepped gear G2. The pinion P1 on the DC motor shaft meshes with the stepped gear G1. The servo consists of four pairs of meshing nylon gears as illustrated in the figure below. The synthetic motor signal data being generated in this example utilizes the servo motor and gear train specifications listed below. For your use case, you can use feature values from a Cloud source or compute them from real-time hardware streaming data. SET UP TIMECONTROL DASHBOARD UPDATESee the example Update RUL Prediction as Data Arrives for using the fit method to initialize model parameters from training (historical) data.įor this example, the next step is to generate synthetic servo motor features and save the values to the ThingSpeak channel as separate fields. ![]() For details of these model parameters, see exponentialDegradationModel.Įxtract the model's prior from the fitted exponential degradation model and add any fixed parameters (e.g., Phi) as part of the state structure. The Prior property of the trained model contains the estimated model parameters Theta, Beta, Rho, etc. The fit command estimates a prior for the model's parameters based on the historical records in training data. You normally do not need to change the Metadata setting when setting up your own ThingSpeak channel.įor this example, assume that an ExponentialDegradationModel was trained with historical data using the fit command. Since this example requires the model state to be stored between evaluations, change the Metadata setting of the channel based on the RUL model for this example. ThingSpeak Dashboard - Remaining Useful Life Estimation The simplified workflow to estimate and display the RUL of the servo motor gear train includes the following steps: Gear fault detection using traditional vibration sensors is challenging, especially in cases where the gear train is not easily accessible for instrumentation with accelerometers or other vibration sensors.įor more information about the data stream and hardware setup, see Motor Current Signature Analysis for Gear Train Fault Detection and Live RUL Estimation of a Servo Gear Train Using ThingSpeak. MCSA has been proven to be ideal for motor fault analysis as only the motor current signal is required for analysis, which eliminates the need for additional and expensive sensing hardware. ![]() MCSA is a useful method for the diagnosis of faults that induce torque or speed fluctuations in the servo gear train, which in turn result in correlated motor current changes. A single feature or a combination of features are used construct a Health Indicator (HI) for subsequent RUL estimation. Motor current signature analysis (MCSA) of the current signal driving a hobby servo motor is used to extract frequency-domain (spectral) features from several frequency regions of interest indicative of motor and gear train faults. The MATLAB scripts run real-time in ThingSpeak.įor this example, a set of synthetic motor current signal data is generated that is used to visualize and predict the RUL of the gear train inside the servo. A ThingSpeak dashboard consists of a ThingSpeak channel set up with relevant data streams and MATLAB analysis scripts. SET UP TIMECONTROL DASHBOARD HOW TOThis example shows how to setup a ThingSpeak™ dashboard to estimate and visualize the Remaining Useful Life (RUL) of a servo motor gear train. ![]()
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