A Deep Autoencoder-Based Convolution Neural Network Framework for Bearing Fault Classification in Induction Motors

Fault diagnosis and classification for machines are integral to condition monitoring in the industrial sector. However, in recent times, as sensor technology and artificial intelligence have developed, data-driven fault diagnosis and classification have been more widely investigated. The data-driven approach requires good-quality features to attain good fault classification accuracy, yet domain expertise and a fair amount of labeled data are important for better features. This paper proposes a deep auto-encoder (DAE) and convolutional neural network (CNN)-based bearing fault classification model using motor current signals of an induction motor (IM). Motor current signals can be easily and non-invasively collected from the motor. However, the current signal collected from industrial sources is highly contaminated with noise; feature calculation thus becomes very challenging. The DAE is utilized for estimating the nonlinear function of the system with the normal state data, and later, the residual signal is obtained. The subsequent CNN model then successfully classified the types of faults from the residual signals. Our proposed semi-supervised approach achieved very high classification accuracy (more than 99%). The inclusion of DAE was found to not only improve the accuracy significantly but also to be potentially useful when the amount of labeled data is small. The experimental outcomes are compared with some existing works on the same dataset, and the performance of this proposed combined approach is found to be comparable with them. In terms of the classification accuracy and other evaluation parameters, the overall method can be considered as an effective approach for bearing fault classification using the motor current signal.

Handling qualities of fixed-pitch, variable-speed multicopters for urban air mobility

Optimisation-based control design techniques are applied to multicopters with variable-RPM rotors. The handling qualities and motor current requirements of a quadcopter, hexacopter and octocopter with equal gross weights (5,360N) and total disk areas (producing a 287N/m $^2$ disk loading) are compared in hover. For axes that rely on the rotor thrust (all except yaw), the increased inertia of the larger rotors on the quadcopter increase the current requirement, relative to vehicles with fewer, smaller rotors. Both the quadcopter and hexacopter have maximum current margin requirements (relative to hover) during a step command in longitudinal velocity. In yaw, rotor inertia is irrelevant, as the reaction torque of the motor is the same whether the rotor is accelerating or overcoming drag. This, combined with the octocopter’s greater inertia as well as the fact that it requires 30% less current to drive its motors in hover, results in the octocopter requiring the greatest current margin, relative to hover conditions. To meet handling qualities requirements, the total weight of the motors of the octocopter and hexacopter is comparable at 13.5% weight fraction, but the quadcopter’s motors are heavier, requiring 16% weight fraction. If the longitudinal and lateral axes were flown in ACAH mode, rather than TRC mode, the total motor weight of all configurations would be nearly identical, requiring about 13.5% weight fraction for motors (compared to 7–9% weight fraction from hover torque requirements).

Diagnostics of actuation system by Hadamard product of integrated motor current residuals applied to electro-mechanical actuators

The paper presents diagnostics methodology that can identify the event of occurrence of fault in the actuator or the linkage system of the flight control actuation system driven by Linear Electromechanical Actuators (LEMA). The standard data analysis like motor current signature analysis (MCSA) is good at identifying the incipient faults within the elements of the actuators in situations where-in the actuators are driving control surfaces. But in back driven cases, where-in LEMA is driven back by control surfaces, the faults outside the LEMAs are difficult to be detected due to higher mechanical advantages of transmission elements like roller screws, gear train and linkage arms scaling down their effects before reaching the motor. One such event occurred in a ground test, wherein the jet vanes were sheared when back driven by excessive gas dynamic forces. Neither the motor current nor the LEMA position feedback data has any clue of the instance of occurrence of such shearing. The case study is discussed in detail and diagnostics solution for such failures is proposed. A new methodology to pin point the event of occurrence is arrived at based on ground static test data of four independent channels. The same is reassured for its applicability using lab experiments on three samples mimicking the failure. The method's applicability is also extended for extracting events in actual flight, by comparing the flight telemetry data with the mimicked lab level (dry runs) data. The methodology uses the analysis of LEMA motor current data to arrive at the vital diagnostic information. The current data of LEMA directly cannot be interpreted due to non-stationary nature arising from variable speed and its pulsating form because of the pulse width modulation (PWM) switching, threshold voltages and closed loop dynamics of the servo. Hence the motor current is integrated using cumulative trapezoidal method. This integrated data is spline curve fitted to arrive at residuals vector. The Hadamard product is used on the residuals vector to amplify the information and suppress the noise. Further, normalizing is done to compare data across tests and samples. With this, necessary diagnostic information was extracted from static test data. The method is extended for extracting diagnostics information from actual flight using comparison analysis of, the test data in actual environment with mimicked lab level dry runs. It is also verified for applicability in faults directly driven by actuators in lab level experiments on three samples.

Research on the Electromagnetic Conversion Method of Stator Current for Local Fault Detection of a Planetary Gearbox

Motor current signature analysis (MCSA) is a useful technique for planetary gear fault detection. Motor current signals have easier accessibility and are free from time-varying transfer path effects. If the fault symptoms in current signals are well understood, it will be more beneficial to develop effective current signal processing methods. Some researchers have developed mathematical models to study the characteristics of current signals. However, no one has considered the coupling of rotor eccentricity and gear failures, resulting in an inaccurate analysis of the current signals. This study considers the sun gear failure of a planetary gearbox and the eccentricity of the motor rotor. An improved induction motor model is proposed based on the magnetomotive force (MMF) to simulate the stator current. By analyzing the current, the modulation relationships of gearbox meshing frequency, fault frequency, power supply frequency, and gear rotating frequency are obtained. The proposed model is validated to some extent using experimental data.

Determining the Electrical Losses in the Electrical Supply Line of a Sprinkler Using Autonomous Asynchronous Generator

The use of an asynchronous generator to power the electrical equipment of the sprinkler is a comprehensive solution that allows you to reduce electrical losses in the supply line. The problem of reactive power compensation for sprinkler machines can be solved by dividing capacitor units into main and additional, the main one is to create the required excitation current in the asynchronous generator, and the additional one is to compensate for the reactive component of the electric motor current. Moreover, an additional unit is installed directly at the outputs of the booster pump to unload the line, and the main capacitor unit is installed near the asynchronous generator.

Improvement of Torque Estimation for Series Viscoelastic Actuator Based on Dual Extended Kalman Filter

Adding damping such as viscoelastic element in series elastic actuators (SEA) can improve the force control bandwidth of the system and suppression of high frequency oscillations induced by the environment. Thanks to such advantages, series viscoelastic actuators (SVA) have recently gained increasing research interests from the community of robotic device design. Due to the inconvenience of mounting torque sensors, employing the viscoelastic elements to directly estimate the output torque is of great significance regarding the real-world applications of SVA. However, the nonlinearity and time-varying properties of viscoelastic materials would degrade the torque estimation accuracy. In such a case, it is paramount to simultaneously estimate the output torque state and viscoelastic model coefficients in order to enhance the torque estimation accuracy. To this end, this paper first completed the design of a rubber-based SVA device and used the Zenner linear viscoelastic model to model the viscoelastic element of the rubber. Subsequently, this paper proposed a dual extended Kalman filter- (DEFK) based torque estimation method to estimate the output torque and viscoelastic model coefficients simultaneously. The noisy observations of two Kalman filters were provided by motor current-based estimated torque. Moreover, the dynamic friction of harmonic drive of the designed SVA was modeled and compensated to enhance the reliability of current-based torque estimation. Finally, a number of experiments were carried out on SVA, and the experimental results confirmed the DEFK effectiveness of improving torque estimation accuracy compared to only-used rubber and only-used motor current torque estimation methods. Thus, the proposed method could be considered as an effective alternative approach of torque estimation for SVA.

Electric Window Regulator Based on Intelligent Control

In order to effectively solve the problem of installation cost of automobile electric windows and the safety of passengers, the window regulator of the car must have an intelligent control function. For example, most automobile windows now have an anti-pinch function. In this paper, the model of DC brushed motor is analyzed, an intelligent control scheme for automotive power windows is proposed, and the relationship between current ripple and window travel, motor current and external resistance are verified. In the hardware design, S9S12G128 is the main control chip, and the motor current acquisition method is designed. In the software design, intelligent control methods such as current integration method, adaptive and self-learning algorithm and intelligent speed regulation method are proposed to realize functions such as automatic window opening and closing, intelligent anti-pinch and intelligent speed regulation. After many experiments, the results prove the feasibility of the above methods and the stability of the system.