Results of performance tests of an experimental class H insulation system for locomotive traction motors and its monitoring using the mobile diagnostics means

Objective: To perform traction electric motor performance tests with an experimental class H heat resistance insulation system. Methods: An experimental set of traction electric motors was manufactured at the Voronezh Diesel Locomotive Repair Plant to be compared with a motor of class F insulation. Results: The performance indicators of controlled locomotives have been summarized. Explanations and definition of additional insulation parameters and an example of their use in case of traction electric motor failure due to damage to controlled locomotives have been given. Practical importance: The proposed methodology for predictive diagnostics of traction electric motors performance can be used in service locomotive depots.

Adaptive model predictive control of a six-rotor electric vertical take-off and landing urban air mobility aircraft subject to motor failure during hovering

In this article, motor failure control of a six-rotor electric vertical take-off and landing (eVTOL) urban air mobility aircraft is investigated using adaptive model predictive control (MPC) based on the linear parameter-varying (LPV) model developed using the nonlinear rigid-body aircraft model. For capturing the aircraft dynamics under motor failure conditions, a family of linearized models are obtained by trimming the nonlinear aircraft model at multiple equilibrium conditions and the LPV model is obtained by linking the linear models using the failed rotor speed, where the system transition from healthy to failure is modeled by a scheduling parameter calculated based on failed rotor speed caused by available motor peak power after failure. The proposed adaptive MPC is developed to optimize the system output performance, including the rigid-body aircraft velocity and altitude, by using quadratic programming optimization with reference compensation subject to a set of time-varying constraints representing the current available propeller acceleration calculated based on the motor power. Simulation study is conducted based on the developed LPV control design and original nonlinear rigid-body model, and the simulation results demonstrate that the designed adaptive MPC controller is able to recover and maintain the aircraft at desired stable condition after motor failure.

Spectrum Analysis for Condition Monitoring and Fault Diagnosis of Ventilation Motor: A Case Study

In modern power systems, since most loads are inductive by nature, there is an ongoing power quality issue and researchers’ interest in improving the power factor is widespread, as inductive loads have a low power factor that depletes the system’s capacity and has an adverse effect on the voltage level. The measurement and acute analysis of voltage- and current-level waveforms is essential to tackle power quality issues. This article presents a detailed case study and analysis of real-time data measured from a frequency converter, which is used to operate the motor of a ventilation system. The output of the frequency converter is a highly distorted current wave. A hybrid Fourier transform (FT)- and wavelet transform-based solution has been proposed here to diagnose and identify the causes of motor failure in the ventilation system. The traditional FT did not give a detailed analysis of this type of signal, which is highly contaminated by noise. Therefore, first, the signal is preprocessed for data denoising using the wavelet transform. Second, the Fourier analysis is performed on the filtered signal for frequency analysis and segregation of fundamental frequency components, higher-order harmonics, and suppressed noise. The spectrum analysis reveals that the noise is generated due to the rapidly switching circuits in the frequency converter and this unfiltered signal at the output of the frequency converter causes motor failure.

Real-Time Prediction of Mud Motor Failure Using Surface Sensor Data Features and Trends

Mud motor failure is a significant contributor to non-productive time in lower-cost land drilling operations, e.g. in North America. Typically, motor failure prevention methodologies range from re-designing or performing sophisticated analytical modeling of the motor power section, to modeling motor performance using high-frequency downhole measurements. In this paper, we present data analytics methods to detect and predict motor failures ahead of time using primarily surface drilling measurements.We studied critical drilling and non-drilling events as applicable to motor failure. The impacts of mud motor stalls and drill-off times were investigated during on-bottom drilling. For the off-bottom analysis, the impact of variations in connection practices (pick up practices, time spent backreaming, and time spent exposing the tools to damaging vibrations) was investigated. The relative importance of the various features found to be relevant was calculated and incorporated into a real-time mud motor damage index.A historical drilling dataset, consisting of surface data collected from 45 motor runs in lateral hole sections of unconventional shale wells drilled in early to mid-2019, was used in this study. These motor runs contained a mix of failure and non-failure cases. The model was found to accurately predict motor failure due to motor wear and tear. Generally, the higher the magnitude of the impact stalls experienced by the mud motor, the greater the probability of eventual failure. Variations in connection practices were found not to be a major wear-and-tear factor. However, it was found that connection practices varied significantly and were often driller-dependent.The overall result shows that simple surface drilling parameters can be used to predict mud motor failure. Hence, the value derived from surface sensor information for mud motor management can be maximized without the need to run more costly downhole sensors. In addition to this cost optimization, drillers can now monitor motor degradation in real-time using the new mud motor index described here.

Analysis and Management of Motor Failures of Hexacopter in Hover

This research presents an analysis and management strategy for hovering hexacopter with one or more failing motors. Of late, multirotor drones have become particularly popular, and all drones have been increasing in popularity. Unlike a fixed-wing drone, failure of motors in a multirotor craft may cause safety problems. Numerous published articles have proposed solving this problem by redesigning the control law or control gain. This approach, however, is difficult to implement because change of control gain usually involves connecting external devices. This paper proposes to keep the control gain unchanged but reallocate the thrusts. Simulations are conducted on a hexacopter in various hovering modes. Some hovering state problems are investigated for the linearized dynamics but also numerically verified for the original nonlinear dynamics. In case some motors of a hexacopter fail in flight, an allocation matrix is proposed to redistribute required thrusts to functional motors. Seven cases of motor failure are studied. This paper analytically proves that limited controllability for emergency landing is feasible in four scenarios at the linear level, but the other three scenarios are completely uncontrollable. Numerical simulations are presented to demonstrate the validity of our algorithm. An online video of real flight also confirms our results. This paper potentially helps the design of failure management of rotors and increases the successful rate of emergent landing.