Search Results (26)

This paper presents the design and implementation of a low-cost microcontroller-based dual-channel fan controller optimized for high-power audio amplifiers, yet adaptable to power supplies, electronic loads, and other thermally intensive systems. Unlike conventional designs that drive all fans uniformly, the proposed solution provides fully independent cooling via dual I²C temperature sensors, predictive trend analysis, and multi-stage hysteresis. The controller incorporates advanced features including an anti-dust startup sequence, predictive boost with latching, active cross-cooling, anti-heat-soak protection, and stall detection via tachometer monitoring, complemented by LED-based fault signaling and automatic channel muting during overheating or fan failure. Hardware support for 12 V and 24 V fans, dual power-input options, and a compact PCB layout enhance integration flexibility. The firmware employs temperature-driven PWM mapping with EMA filtering and multi-level hysteresis. The experimental results confirm that all implemented features operate as intended, with each function demonstrating clear practical relevance, whether in improving responsiveness, preventing heat accumulation, or enhancing system reliability under a wide range of operating conditions. Full article

A machine learning (ML) methodology for the robust detection and severity characterization of incipient gear faults under variable speed and load is postulated. The methodology is trained using vibration signals from a single accelerometer mounted on the gearbox, simultaneously acquired with tachometer signals at a sample of working conditions (WCs) from the range of interest. A special parametric identification procedure of gearbox dynamics that may account for the continuous range of WCs is introduced through ‘clouds’ of advanced stochastic data-driven Functionally Pooled models, estimated from angularly resampled vibration signals. Each cloud represents the gearbox dynamics at a specific fault severity level, while the pseudo-static effects of the WCs on the dynamics are accounted for through data pooling. Fault detection and severity characterization are achieved by testing the consistency of a vibration signal with each model cloud within a hypothesis testing framework in which the unknown load is also estimated. The methodology is assessed through 18,300 experiments on a single-stage spur gearbox including four incipient single-tooth pinion faults, 61 speeds, and four load levels. The faults produce no significant changes in the time-domain signals, while their frequency-domain effects overlap with the variations caused by the WCs, rendering the diagnosis problem highly challenging. The comparison with a state-of-the-art deep Stacked Autoencoder (SAE) demonstrates the ML method’s superior performance, achieving 95.4% and 91.6% accuracy in fault detection and characterization, respectively. Full article

This paper presents a physics-informed, tacholess pipeline for smartphone-grade Noise, Vibration, and Harshness (NVH) diagnosis in electric vehicle powertrains. A configurable generator synthesizes labeled signals with order components (1×/2×/3×), AM/FM modulation, sub-harmonics, impact-driven ring-down near resonance, and realistic white/pink/ambient noise at phone bandwidths. A ridge-guided harmonic comb recenters orders without a tachometer and splits tonal from residual content. Interpretable features—order-invariant ratios ($E_{2\times }/E_{1\times }$, ${\mathrm{S}\mathrm{B}}_1$/$E_{1\times }$, $E_{0.5\times }/E_{1\times }$) and residual descriptors (band-power, kurtosis, cepstrum/WPT)—feed light-compute models. A reproducible benchmark stresses SNR (−5…+10 dB), RPM profiles (ramp/steps/cycles), and simulated domain shift; parameter-to-feature analyses (with Sobol sensitivity and a delta-method identifiability proxy) quantify measurability under phone constraints. Across a five-fold CV, tacholess order tracking increases tonal SNR by ≥+6 dB and yields macro-F1 ≈ 0.86 with Random Forest, while ordinal severity achieves QWK ≈ 0.81 (ECE ≈ 0.06) and regression attains MAE ≈ 0.12 (R² ≈ 0.78). All code, datasets, figures, and tables regenerate from fixed seeds with one-command builds; a data card and a sim-to-real guide are included. The result is an open, low-compute standard that couples reproducibility with physics-aligned interpretability, providing a practical baseline for EV NVH diagnostics with smartphones and a common ground for future field validation. Full article

Estimating the absolute scale of reconstructed camera trajectories in monocular odometry is a challenging task due to the inherent scale ambiguity in any monocular vision system. One promising solution is to fuse data from different sensors, which can improve the accuracy and precision of scale estimation. However, this approach often requires additional effort in sensor design and data processing. In this paper, we propose a novel method for fusing single-camera data with wheel odometer readings using a fuzzy system. The architecture of the fuzzy system has as inputs the wheel odometer value and the translation and rotation obtained from ORB-SLAM2. It was trained with the ANFIS tool in MATLAB 2014b. Our approach yields significantly better results compared to state-of-the-art pure monocular systems. In our experiments, the average error relative to GPS measurements was only four percent. A key advantage of this method is the elimination of the sensor calibration step, allowing for straightforward data fusion without a substantial increase in data processing demands. Full article

This article describes a machine vision-based method for measuring the angular velocity of a rotating disk to characterize the viscous resistance of a ball bearing. A bright marker was attached to a disk connected to a shaft supported by two ball bearings. Rotation of the marker was recorded with a digital camera. A simple algorithm was developed to track the trajectory of the marker and calculate angular displacement of the disk. For accurate detection of the rotating marker, the algorithm employed Multi-Otsu thresholding and the Least Squares Method (LSM). Verification of the proposed method was carried out through a direct comparison between the predicted rotational speeds and measured ones by a commercial tachometer. It was demonstrated that the percentage error of the proposed method was less than 1.75 percent. The evolution of angular velocity after motor power-off was measured and found to follow an exponential decay law. The exponent was found to remain consistent regardless of the induced rotational speed. This proposed measurement method will offer a simple and accurate non-contact solution for monitoring angular velocity and characterizing the resistance of a bearing. Full article

For the purpose of reducing the cost, size, and weight of the integrated navigation system of an AUV (autonomous underwater vehicle), and improving the stealth of this system, an integrated navigation algorithm based on a propeller tachometer is proposed. The algorithm consists of five steps: ① establishing the resistance model of AUV, ② establishing the thrust model, ③ utilizing the measured speeds obtained from the AUV’s voyage trials for calibration, ④ discrimination and replacement of outliers from the tachometer measurements, and ⑤ establishing a linear Kalman filter (LKF) with water currents as state variables. This paper provides the modeling procedure, formula derivations, model parameters, and algorithm process, etc. Through research and analysis, the proposed algorithm’s accuracy has been improved. The specific values of the localization error are detailed in the main text. Therefore, the proposed algorithm has high accuracy, a strong anti-interference capability, and good robustness. Moreover, it exhibits certain adaptability to complex environments and value for practical engineering. Full article

In this work, a tachometer based on a Metglas/PZT/Metglas magnetoelectric (ME) composite was developed to achieve high-precision rotational speed measurement over a wide temperature range (−70 °C to 160 °C). The tachometer converts external magnetic signals into electrical signals through the ME effect and operates stably in extreme temperature environments. COMSOL Multiphysics software was used for simulation analysis to investigate the ME response characteristics of the composite in such environments. To evaluate the properties of the ME composite under different conditions, its response characteristics at various frequencies, DC bias, and temperatures were systematically investigated. A permanent magnet and a DC motor were used to simulate gear rotation, and the voltage output was analyzed by adjusting the position between the sensor and the DC motor. The results show that the measured values of the ME tachometer closely match the set values, and the tachometer demonstrates high measurement accuracy within the range of 480 to 1260 revolutions per minute (rpm). Additionally, the properties of the ME composite at different temperatures were examined. In the temperature range from −70 °C to 160 °C, the ME coefficients exhibit good regularity and stability, with the measured trend closely matching the simulation results, ensuring the reliability and accuracy of the ME tachometer. To verify its practicality, the measurement capability of the ME tachometer was comprehensively tested under extreme temperature conditions. The results show that in high-temperature environments, the tachometer can accurately measure speed while maintaining a high signal-to-noise ratio (SNR), demonstrating excellent anti-interference ability. The proposed ME tachometer shows promising application potential in extreme temperature conditions, particularly in complex industrial environments that require high reliability and precision. Full article

Variable frequency drives (VFDs) are widely used in industry as an efficient means to control the rotational speed of AC motors by varying the supply frequency to the motor. VFD signatures can be detected in vibration signals in the form of sidebands (modulations) induced on tonal components (carrier frequencies). These sidebands are spaced at twice the “pseudo line” VFD frequency, as the magnetic forces in the motor have two peaks per current cycle. VFD-related signatures are generally less susceptible to interference from other mechanical sources, making them particularly useful for deriving speed variation information and obtaining a “pseudo” tachometer from the motor’s synchronous speed. This tachometer can then be employed to accurately estimate the speed profile and to facilitate order tracking in mechanical systems for vibration analysis purposes. This paper presents a signal processing technique designed to extract a pseudo tachometer from the VFD signature found in a vibration signal. The algorithm was tested on publicly available vibration data from a test rig featuring a two-stage gearbox with seeded bearing faults operating under variable-speed conditions with no load, i.e., with minimal slip between the induction motor’s synchronous and actual speed. The results clearly demonstrate the feasibility of using VFD signatures both to extract an accurate speed profile (root mean square error, RMSE of less than 2.5%) and to effectively perform order tracking, leading to the identification of bearing faults. This approach offers an accurate and reliable tool for the analysis of vibration in mechanical systems driven by AC motors with VFDs. However, it is important to note that some inaccuracies may occur at higher motor slip levels under heavy or variable loads due to the mismatch between the synchronous and actual speeds. Slip-induced variations can further distort tracked order frequencies, compromising the accuracy of vibration analysis for gear mesh and bearing defects. These issues will need to be addressed in future research. Full article

Artificial intelligence has succeeded in many different areas in recent years. Especially the use of machine learning algorithms has been very popular in all areas, including fault detection. This paper explores a case study of applying machine learning techniques and neural networks to detect ten different machinery fault conditions using publicly available data sets collected from a tachometer, two accelerometers, and a microphone. Ten different conditions were classified using machine learning algorithms. Fifty-eight different features are extracted from time and frequency by applying the Short-Time Fourier Transform to the data with the window size of 1000 samples with 50% overlap. The Support Vector Machine models provided fault classification with 99.8% accuracy using all fifty-eight features. The proposed study explores the dimensionality reduction of the extracted features. Fifty-eight features were ranked using the Decision Tree model to identify the essential features as the classifier predictors. Based on feature extraction and raking, eleven predictors were extracted leading to reduced training complexity, while achieving a high classification accuracy of 99.7% could be obtained in less than half of the training time. Full article

Accurate shaft speed extraction is crucial for synchronous sampling in the fault diagnosis of wind turbines. However, traditional narrow-bandpass filtering techniques face limitations when dealing with large fluctuations in rotational speed, hindering the accurate construction of an instantaneous phase for synchronous resampling of a shaft. To overcome this, we propose a tachometer-less synchronous sampling based on Scaling-Basis Chirplet Transform, tailored to a wind turbine’s structure and operating conditions. The algorithm generates a time–frequency representation of the vibration response, revealing time-varying characteristics even under large speed fluctuations. Using maximum tracking on the time–frequency spectrum, we extract instantaneous speed and compare its accuracy with tachometer-acquired results. The instantaneous phase is obtained through numerical integration, and vibration data are resampled synchronously using inverse function interpolation in the digital domain. Numerical simulations and practical cases of wind turbines demonstrate the effectiveness and the engineering applicability of our methodology. Full article

Rolling element bearing (REB) vibration signals under variable speed (VS) have non-stationary characteristics. Order tracking (OT) and time-frequency analysis (TFA) are two widely used methods for REB fault diagnosis under VS. However, the effect of OT methods is affected by resampling errors and close-order harmonic interference, while the accuracy of TFA methods is mainly limited by time-frequency resolution and ridge extraction algorithms. To address this issue, a novel method based on envelope spectrum fault characteristic frequency band identification (FCFBI) is proposed. Firstly, the characteristics of the bearing fault vibration signal’s envelope spectrum under VS are analyzed in detail and the fault characteristic frequency band (FCFB) is introduced as a new and effective representation of faults. Then, fault templates based on FCFB are constructed as reference for fault identification. Finally, based on the calculation of the correlation coefficients between the envelope spectrum and fault templates in the extended FCFB, the bearing fault can be diagnosed automatically according to the preset correlation coefficient criterion. Two bearing VS experiments indicate that the proposed method can achieve satisfactory diagnostic accuracy. The comparison of OT and TFA methods further demonstrates the comprehensive superiority of the proposed method in the overall consideration of accuracy, diagnostic time, tachometer dependency, and automatic degree. Full article

This study presents a pointer-driven controller for an instrument panel. The proposed pointer utilizes the permanent magnet (PM) stepping motor produced by the Japanese company NMB. This stepping motor is vibration-proof and tolerates noise jamming as well as wind and rain exposure. Moreover, it has no mechanical structures and is low cost. Most importantly, it features accurate positioning; therefore, it can be used to measure vehicle speed, engine speed, fuel capacity, and temperature. However, the PM stepping motor of the NMB pointer requires 10 degrees for each step, and this low resolution results in roll hesitation as its steps. The aim of the current paper was to solve the problems of the large angle size and low resolution associated with this stepping motor. Based on two-phase excitation, we propose driving the motor using pulse width modulation (PWM). Specifically, we divided each 10-degree step into 100 equal parts. In other words, every step is 0.1 degrees. The resolution of pointer rotation can be increased by 100-fold by using the approach proposed in this paper. When applied to vehicle (or locomotive) instruments, the pointer can move very smoothly on the tachometer or oil gauge. Full article

Computer vision techniques can monitor the rotational speed of rotating equipment or machines to understand their working conditions and prevent failures. Such techniques are highly precise, contactless, and potentially suitable for applications without massive setup changes. However, traditional vision sensors collect a significant amount of data to process and measure the rotation of high-speed systems, and they are susceptible to motion blur. This work proposes a new method for measuring rotational speed processing event-based data applied to high-speed systems using a neuromorphic sensor. This sensor produces event-based data and is designed to work with high temporal resolution and high dynamic range. The main advantages of the Event-based Angular Speed Measurement (EB-ASM) method are the high dynamic range, the absence of motion blurring, and the possibility of measuring multiple rotations simultaneously with a single device. The proposed method uses the time difference between spikes in a Kernel or Window selected in the sensor frame range. It is evaluated in two experimental scenarios by measuring a fan rotational speed and a Router Computer Numerical Control (CNC) spindle. The results compare measurements with a calibrated digital photo-tachometer. Based on the performed tests, the EB-ASM can measure the rotational speed with a mean absolute error of less than 0.2% for both scenarios. Full article

This paper presents a new synthetic dataset obtained from Gazebo simulations of an Unmanned Ground Vehicle (UGV) moving on different natural environments. To this end, a Husky mobile robot equipped with a tridimensional (3D) Light Detection and Ranging (LiDAR) sensor, a stereo camera, a Global Navigation Satellite System (GNSS) receiver, an Inertial Measurement Unit (IMU) and wheel tachometers has followed several paths using the Robot Operating System (ROS). Both points from LiDAR scans and pixels from camera images, have been automatically labeled into their corresponding object class. For this purpose, unique reflectivity values and flat colors have been assigned to each object present in the modeled environments. As a result, a public dataset, which also includes 3D pose ground-truth, is provided as ROS bag files and as human-readable data. Potential applications include supervised learning and benchmarking for UGV navigation on natural environments. Moreover, to allow researchers to easily modify the dataset or to directly use the simulations, the required code has also been released. Full article

Self-powered triboelectric tachometers have wide application prospects in mechanical and electrical industries. However, traditional disc-type tachometers typically require large contact force, which burdens rotary load and increases frictional wear. To reduce the friction force of triboelectric tachometers, we present an alternative structure defined by flapping between rigid and flexible triboelectric layers. In this work, we further characterize this type of tachometer, with particular focus on the oscillating relationship between output voltage and rotation speed due to the plucking mechanism. This oscillating relationship has been demonstrated both theoretically and experimentally. For future self-powered triboelectric tachometers, the proved oscillating relationship can be applied as calibration criteria for further enhancing sensitivity and linearity in rotation measurement. Full article