Solutions for Torque and Speed Measurement on Electric Machine Controllers Test Benches

To design motor drives for optimal motion control, and to obtain precise Brushless DC motor model, accurate measurement of motor parameters like phase current, speed and torque are required. Speed and current are also required to provide feedback between the BLDC motor and its controller to develop a robust BLDC motor control algorithm. In this paper, phase current, speed and torque measurement technique for Hub BLDC motor is presented to analyze its performance for low power electric vehicle applications. For the phase current measurement, the Hall-Effect current sensor ACS712 is used. The speed of the motor is calculated using an internal hall sensor that is connected to dsPIC33F’s input capture pin that generates an interrupt whenever a change in the hall sensor signal value occurs. Further, an experimental setup is established in the laboratory to measure the torque at varying load and to determine the relationship between motor torque, input current, output power, speed and efficiency.

This paper presents a strategy to emulate a reduced scale of sucker rod pump using laboratory test bench. The strategy is based on combination of software and hardware system. Torque reference is generated using the proposed strategy based on real time model of sucker rod pump that runs in LabVIEW. The output of encoder and torque sensor are integrated through NI PCI- 6221 data acquisition card to the real time platform. The performance of the experimental test bench is evaluated using current, voltage, speed, and torque measurements and power calculations. The experimental results are compared to simulation results. The results indicate the capability of this test bench to serve as a platform for conducting experimental studies on sucker rod pump.

Testing electric machines during production and after overhauling is one of the basic production stages, which has a direct effect on the quality of the final products. Increasing the level of automation of the testing process to the point of total elimination of the human factor has been an unsolved problem until recently. On commission from ZAO Transmashkholding and OOO Novocherkassk Electric Locomotive Plant (NEVZ) and jointly with the latter, that task was accomplished for the entire range of the traction and auxiliary motors produced by this plant. The specifics of the methods for testing dc and ac electrical machines in accordance with the regulations of the normative documentation—in particular, those that concern the loading conditions—brings about difficulties in the creation of power static converters and automation equipment for test benches to test dc and ac electrical machines. The modes of loading the machines in accordance with a back-to-back diagram required tailored power transformers and converters. The creation of automatic control systems for power equipment—in particular, automated means for measuring the electric and the mechanic parameters of the testing process—necessitated development of complex systems that operate on the “conveyor belt” principle. All safety requirements were met, easy operation was ensured for personnel, the risk of committing errors was minimized, and an acceptable rate of the testing cycle was guaranteed. Operation of the electrical machine test facility at OOO NEVZ over a number of years has proven the effectiveness of the solutions that have been developed. This article describes the experience of developing and producing electrical equipment and automatic control systems for automated test benches to test electrical machines, methods for solving problems that arise when designing such equipment, and examples of different approaches to the design depending on production specifics. Circuit diagrams of the electrical equipment for industrial tests of traction electric motors are provided.

Flow and free running speed characterization of dental air turbine handpieces

A Simple Multi-Motor Drive with Converter-Fed Synchronous Machines with AC-Excitation

Industrial predictive maintenance programs often rely on SCADA historian signals characterized by low-frequency sampling and asynchronous reporting intervals. These data constraints, specifically non-uniform scan rates and inter-tag time misalignment, limit the applicability of high-resolution or sensor-intensive prognostic models. This study proposes a lightweight, physics-informed health proxy, the temperature-weighted work (TWW) index, designed to monitor motor stator-winding degradation within these industrial limitations. The TWW index accumulates mechanical work derived from torque and speed measurements, weighted by an adaptive exponential temperature-emphasis function that penalizes operation at elevated temperatures. The formulation is inspired by practical thermal-aging heuristics such as Montsinger’s rule in the qualitative sense that higher temperatures are treated as disproportionately more damaging, but it is not intended as a direct implementation of a fixed absolute-temperature life law. Instead, it is designed as a lightweight adaptive index suitable for online SCADA-based implementation. To address SCADA-specific irregularities, the framework incorporates data synchronization and resampling techniques to align heterogeneous tags, alongside power-thresholding to isolate degradation-relevant load periods. The resulting cumulative index is mapped to a normalized health/RUL proxy using failure-referenced thresholds identified from historical events. Validation using field data from industrial three-phase motors demonstrates that the TWW index provides a monotonic degradation profile that is consistent with documented winding-related failures and proactive removals. Case studies confirm that the model enabled proactive maintenance interventions by signaling the terminal phase of insulation life before catastrophic breakdown, offering a hardware-free and scalable solution for real-time asset management.

Online Inertial Parameter Estimation for Robotic Loaders

Electrical machine control is the essential technology in motion control field. Electrical machine is adding the network communication module in their traditional control unit, while motion control is stretching its way toward remote motion control domain. The Web-based network motion control is really a way to develop remote motion control in an open area; it gives more chances to operate and update the motion control equipments far from the workshop. The author aims at applying an extending network communication module to the traditional electrical machine control unit and some special problems aroused from making use of J2EE Platform technology in electrical machine control, such as the interface between software and control unit of electrical machine control, and the server of the remote electrical machine control. In this paper a subminiature permanent DC motor is connected to the server via RS232 serial port and is also used as an object for remote control, the author present the difficulties in building the B/S (Browser/Server) structure which can operate the electric machine through the Internet far from our lab, and gives some possible ways and some novel ideal to solve the problem

This study established a nonlinear control design for switched reluctance motor (SRM) vehicle applications, using the backstepping approach. The suggested controller is established according to a model that consider magnetic saturation while reducing torque ripple and resulting in less vibrations. To optimize torque ripple, control angles are adjusted based on the machine speed and torque measurements. Indeed, a lookup table is constructed, offering the efficient control angles for various motor operating points. The suggested control technique was validated through simulation, exploiting an accurate MATLAB SRM model considering magnetic saturation effects. To illustrate the superiority of the suggested regulator, a comparison of its performance with a proportional-integral (PI) controller was performed. The acquired findings indicate the suggested regulator’s effectiveness.

Multi-wheel mobile robotic vehicles require speed and torque measurements, which are constrained within practical error bounds. The paper systematically analyses the multiple sensor uncertainty and guarantees bounded input signals for the general case of a PMDC actuator within the context of wheel electrically actuated vehicles. For such vehicles often complex sensors and feedback systems are required to continuously monitor changes on the state variables, speed and torque and compensate accordingly. The paper proposes a bounded sensor error inclusion in the model analysis. The resulting mathematical formulation results in the derivation of the actuation signals with guaranteed intervals. The analyses also extend to the power converter operating at Q1-mode and clearly shows the duty-cycle variations and how this can affect the actuator system and consequently the robotic vehicle.

When designing an electric motor drive for a conversion of bicycle to an electric vehicle it is obligatory to determine the required power of the electric motor and the energy of a battery pack. By using a classical method, drive requirements can be established with speed and torque measurement on the drive wheel of the vehicle using a torque meter. The given traction profile is used for the design of the drive. This paper describes the method for estimation of power and required energy of vehicle only by means of global positioning system (GPS). When selecting a GPS device, it is necessary to take the resolution of the device and accuracy of vehicle speed and altitude into account. Using the measured track (speed and altitude profile) and the drag model of the vehicle it is possible to calculate both power that the vehicle develops, and energy consumed for a certain distance. The GPS device measurements, selection of electric drive, bicycle electrification and analysis of processed KTM Knoxville electric bicycle is presented. Keywords: GPS device, longitudinal speed, vehicle drag, estimation, bicycle electrification

In this paper, the load torque is measured using sensorless online algebraic approach (SAA) and sensorless reduced order observer (SROO) scheme without torque as well as speed sensor. The measured torque is used in controlling the speed of DC motor connected through a partially flat boost converter using the exact tracking error dynamics passive output feedback (ETEDPOF) control law. Further, SAA is compared with SROO scheme for frictional, propeller, fan type, and unknown load torques. The results obtained confirm the reduction in cost of due to the indirect measurement of load torque and speed.

This study mainly focuses on the accurate estimation of the output shaft torque - and thus the clutch torque - of the automated manual transmissions as well as dual clutch transmissions, using the data available in the current production vehicles. The required information includes the nominal engine torque map and measurements of engine speed, clutch speed, and wheel speed. Utilizing the above-mentioned information, based on the design of four types of observers (unknown input observer, shaft compliance observer, synthesis of these two types of observers, and an adaptive observer), several methods to obtain the torque estimation without additional cost are developed to improve the feedback control performance of the clutch actuators and thus the vehicle launching and gear shift qualities. The stability of the proposed observers is investigated, and a set of assessments to confirm the performance of the observer system is arranged via simulation.

Drilling rate of penetration (ROP) is a major contributor to drilling costs. ROP is influenced by many different controllable and uncontrollable factors that are difficult to distinguish with the naked eye. Thus, machine learning (ML) models such as neural networks (NN) have gained momentum in the drilling industry. Existing models were either field-based or tool-based, which impacted the accuracy outside of the trained field. This work aims to develop one generally applicable global ROP model, reducing the effort needed to re-develop models for every application. A drilling dataset was gathered from exploration and development wells in both onshore and offshore operations from a variety of fields and regions. The wells were curated to have different water depths, down hole drive such as Rotary Steerable System (RSS), PDM, Standard Rotary, bit types (Mill Tooth, TCI, PDC) and inclinations (vertical or deviated). A deep neural network was used for modelling the relationship between ROP and inputs taken from real-time surface data, such as Torque, Weight-on-Bit (WOB), rotary speed (RPM), flow and pressure measurements. The performance of the ROP model was analyzed using historical data via summary statistics such as Mean Absolute Percentage Error, as well as graphical results such as residuals distributions, cumulative distribution functions of errors, and plots of ROP vs depth for independent holdout testing wells not included in the model fitting process. Analysis was done both in aggregate, and for each specific well. The ROP model was demonstrated to generalize effectively in all cases, with only minor increases in error metrics for the holdout test wells, where the Mean Absolute Percentage Error averaged across wells was ~20%, compared to 17.5% averaged across training wells. Furthermore, residuals distributions were centered close to zero, indicating low systematic error. This work proves the case for a "global" ROP prediction model applicable "out-of-the-box" to a broad set of drilling operations. A global ROP model has the potential to eliminate learning curves, reducing time and costs associated with having to develop a new model for every field. Furthermore, a model that effectively captures the relationships between parameters controllable by drillers and ROP can be used for automatically identifying drilling parameters that improve ROP. Preliminary field-testing of the ROP optimization system yielded positive results, with many examples of increased ROP realized after following drilling parameter recommendations provided by the software.

This study investigated whether pressure changes common to scuba diving and to hyperbaric oxygen therapy would not cause crush damage or leakage from critical seals in commercially available cochlear implants. The implanted packages of cochlear implants are susceptible to electrical failure caused by leakage from critical seals and to crush injury when exposed to changing barometric pressures encountered in recreational diving and in hyperbaric oxygen therapy. Six Clarion 1.2, eight MED-EL Combi-40+, six Nucleus CI22M, and six Nucleus CI24M cochlear implants underwent three exposures at 165 feet of seawater (FSW) (6 ata abs), 99 FSW (4 ata abs), and 60 FSW (2.8 ata abs), simulating rates in accordance with U.S. Navy dive tables for nondecompression dives. Dives to 45 FSW (2.4 ata abs) simulated wound therapy. Before each dive began, after each dive, and after completion of the dive protocol, each device underwent telemetry and electrical integrity checks. All implants were returned to their respective factories for final electrical and quality control testing. All 26 devices completed the dive protocol. One Nucleus CI24M implant had a fault recorded at electrode lead 18 on predive and final product testing, which was absent during interval dive measurements. All 26 devices passed final electrical and quality control testing. In addition, the six Clarion units passed repeat helium leak testing. The implanted components of the Clarion 1.2, MED-EL Combi-40+, and Nucleus CI22M and CI24M were safely subjected to repeated pressure changes up to 6 atm abs, equivalent to 165 feet of seawater, without electrical failure from leakage at critical seals or crush damage.