Search Results (97)

Inelastic electron tunneling spectroscopy (IETS) has emerged as a powerful vibrational spectroscopy technique for molecular electronic junctions, providing unique insights into molecular vibrations and electron–phonon coupling at the nanoscale. In this review, we present a comprehensive overview of IETS in molecular junctions, tracing its development from foundational principles to the latest advances. We begin with the theoretical background, detailing the mechanisms by which inelastic tunneling processes generate vibrational fingerprints of molecules, and highlighting how IETS complements optical spectroscopies by accessing electrically driven vibrational excitations. We then discuss recent progress in experimental techniques and device architectures that have broadened the applicability of IETS. Central focus is given to emerging applications of IETS over the last decade: molecular sensing (identification of chemical bonds and conformational changes in junctions), thermoelectric energy conversion (probing vibrational contributions to molecular thermopower), molecular switches and functional devices (monitoring bias-driven molecular state changes via vibrational signatures), spintronic molecular junctions (detecting spin excitations and spin–vibration interplay), and advanced data analysis approaches such as machine learning for interpreting complex tunneling spectra. Finally, we discuss current challenges, including sensitivity at room temperature, spectral interpretation, and integration into practical devices. This review aims to serve as a thorough reference for researchers in physics, chemistry, and materials science, consolidating state-of-the-art understanding of IETS in molecular junctions and its growing role in molecular-scale device characterization. Full article

This study reports the discovery and structural characterization of a novel polymorph, designated as Form III, of Alclometasone dipropionate, a corticosteroid commonly used in the treatment of inflammatory dermatoses. Form III was obtained by modifying the crystallization conditions reported in prior art and was thoroughly characterized using Powder X-ray Diffraction (PXRD), Fourier Transform Infrared (FT-IR) spectroscopy, melting-point determination, Differential Scanning Calorimetry (DSC), Thermogravimetric Analysis (TGA), including its first derivative (DTG), optical microscopy, and Scanning Electron Microscopy (SEM). In parallel, pure Form II, previously observed only in mixtures with Form I, was successfully isolated and characterized using the same analytical techniques. Both forms were compared in terms of structural, thermal, and morphological properties. PXRD analysis revealed that Form III crystallizes in a triclinic system; FT-IR spectroscopy revealed unique vibrational signatures, and microscopy showed rod-like crystal morphology. The discovery of Form III expands the current understanding of the solid-state landscape of Alclometasone dipropionate and opens opportunities for the identification of new industrial purification methods for the compound. Full article

The purpose of this paper is to present vibration-based condition monitoring of forced-draft fans used in sugar factories. The draft system’s uninterrupted operation is essential for the flawless operation of boilers. Considering its importance, a forced-draft fan was employed as a case study. The vibration and noise in the time and frequency domain, along with the overall vibration and noise levels, were measured from the driving and non-driving ends of forced-draft fans at different intervals of time so that errors in measurement could be avoided. These vibration data were analyzed to identify faults in the different components of the forced-draft fans, along with problems in their operation. The results of this analysis indicate that the fans under study produced more noise and vibration than the recommended standard value. Also, through signature analysis, it was found that the fans needed to be balanced and aligned properly. The problems observed were rectified, and recommendations are given for the proper maintenance of these fans. An effort was made to explore the relationship between patterns of the vibration spectrum and signs of failure in a forced-draft fan. It was found that vibration-based condition monitoring is an effective tool in sugar factories. Full article

This article addresses the issue of the elevator cabin door drive system failure diagnosis. The analyzed component is one of the most critical and the most vulnerable part of the entire elevator. Existing solutions in the literature include methods such as spectral analysis of system vibrations, motor current signature analysis, fishbone diagrams, fault trees, multi-agent systems, image recognition, and machine learning techniques. However, there is a noticeable gap in comprehensive studies that specifically address classification of the multiple types of system components failures, class imbalance in the dataset, and the need to reduce data transmitted over the elevator’s internal bus. The developed diagnostic system measures the drive system’s parameters, processes them to reduce data, and classifies 11 device failures. This was achieved by constructing a test bench with a prototype cabin door drive system, identifying the most frequent system faults, developing a data preprocessing method that aggregates every driving cycle to one sample, reducing the transmitted data by 300 times, and using machine learning for modeling. A comparative analysis of the fault detection performance of seven different machine learning algorithms was conducted. An optimal cross-validation method and hyperparameter optimization techniques were employed to fine-tune each model, achieving a recall of over 97% and an F1 score approximately 97%. Finally, the developed data preparation method was implemented in the cabin door drive controller. Full article

We present a thorough machine-learning framework based on real-time state-of-polarization (SOP) monitoring for robust anomaly identification in optical fiber networks. We exploit SOP data under three different threat scenarios: (i) malicious or critical vibration events, (ii) overlapping mechanical disturbances, and (iii) malicious fiber tapping (eavesdropping). We used various supervised machine learning techniques like k-Nearest Neighbor (k-NN), random forest, extreme gradient boosting (XGBoost), and decision trees to classify different vibration events. We also assessed the framework’s resilience to background interference by superimposing sinusoidal noise at different frequencies and examining its effects on the polarization signatures. This analysis provides insight into how subsurface installations, subject to ambient vibrations, affect detection fidelity. This highlights the sensitivity to which external interference affects polarization fingerprints. Crucially, it demonstrates the system’s capacity to discern and alert on malicious vibration events even in the presence of environmental noise. However, we focus on the necessity of noise-mitigation techniques in real-world implementations while providing a potent, real-time mechanism for multi-threat recognition in the fiber networks. Full article

Two different magnetoelastic Metglas materials with distinct shapes were compared as sensing elements for the structural health monitoring of concrete beams. One had a ribbon shape, while the other had a microwire shape. The sensing elements were attached to different concrete beams, and a crack was introduced into each beam. The beams were subjected to flexural vibrations, and their deformations were recorded wirelessly by coils, detecting the magnetic signals emitted due to the magnetoelastic nature of the sensors. Fast Fourier Analysis of the received signal revealed the bending mode frequencies of the beams, which serve as a “signature” of their structural health. In these spectra, the ribbon-shaped sensor exhibited a 1.4-times stronger signal than the microwire sensor. However, the extracted mode frequencies were nearly identical, with differences of less than 1% both before and after damage. This indicates that both sensors can be used equivalently to monitor structural damage in concrete beams. The damage-related relative frequency shifts ranged from −0.01 to −0.03, with similar results for both sensors. Thermal annealing was also studied and appeared to significantly enhance the signal by 10–30%, likely due to the relaxation of internal stresses induced during the rapid solidification synthesis of these materials. This enhancement was more pronounced in the ribbon-shaped sensor. This study is the first to utilize a magnetoelastic microwire sensor for damage detection in concrete beams. Full article

We present a comprehensive vibrational spectroscopic analysis of vertical molecular junction devices constructed using single-layer graphene electrodes separated by an aryl alkane monolayer. In this work, inelastic electron tunneling spectroscopy (IETS) is employed to probe molecular vibrations within the junction, providing an in situ fingerprint of the molecules. Graphene has emerged as a promising electrode material for molecular electronics due to its atomically thin, mechanically robust nature and ability to form stable contacts. However, prior to this study, the vibrational spectra of molecules in graphene-based molecular junctions had not been fully explored. Here, we demonstrate that vertically stacked graphene electrodes can be used to form stable and reproducible molecular junctions that yield well-resolved IETS signatures. The observed IETS spectra exhibit distinct peaks corresponding to the vibrational modes of the sandwiched aryl alkane molecules, and all major features are assigned through density functional theory calculations of molecular vibrational modes. Furthermore, by analyzing the broadening of IETS peaks with temperature and AC modulation amplitude, we extract intrinsic vibrational linewidths, confirming that the spectral features originate from the molecular junction itself rather than extrinsic noise or instrumental artifacts. These findings conclusively verify the presence of the molecular layer between graphene electrodes as the charge transport pathway and highlight the potential of graphene–molecule–graphene junctions for fundamental studies in molecular electronics. Full article

Misalignment faults in drive systems occur when the motor and load are not properly aligned, leading to deviations in the centerlines of the coupled shafts. These faults can cause significant damage to bearings, shafts, and couplings, making early detection essential. Traditional diagnostic techniques rely on vibration monitoring, which provides insights into both mechanical and electromagnetic fault signatures. However, its main drawback is the need for external sensors, which may not be feasible in certain applications. Alternatively, motor current signature analysis (MCSA) has proven effective in detecting faults without requiring additional sensors. This study investigates misalignment faults in synchronous reluctance motors (SynRMs) by analyzing both vibration and current signals under different load conditions and operating speeds. Fast Fourier transform (FFT) is applied to extract characteristic frequency components linked to misalignment. Experimental results reveal that the amplitudes of rotational frequency harmonics ($1x{f}_r$, $2x{f}_r$, and $3x{f}_r$) increase in the presence of misalignment, with $1x{f}_r$ exhibiting the most stable progression. Additionally, acceleration-based vibration analysis proves to be a more reliable diagnostic tool compared to velocity measurements. These findings highlight the potential of combining current and vibration analysis to enhance misalignment detection in SynRMs, improving predictive maintenance strategies in industrial applications. Full article

In induction motor bearings, distributed faults are prevalent, often resulting from factors such as inadequate lubrication and particle contamination. Unlike localized faults, distributed faults produce complex and unpredictable motor signal behaviors. Although existing research predominantly addresses localized faults in mains-fed motors, particularly single-point defects, a comprehensive investigation into particle contamination in bearings of inverter-fed motors is essential for a more accurate understanding of real-world bearing issues. This paper conducts a comparative analysis of vibration, stator current, speed, and acoustic signals to detect particle contamination through signal analysis across three domains: time, frequency, and time-frequency. These domains are analyzed to assess and compare the characteristics of each monitored signal in the context of bearing wear detection. The data were collected from both steady-state and startup transients of an induction motor controlled by a variable frequency drive. The experimental results highlight the most significant characteristics of each monitored signal, evaluated across the different domains of analysis. The primary conclusion indicates that, in inverter-fed motors, sound and vibration signals exhibit abnormal levels when the bearing is damaged but the related-fault signature becomes complicated. Additionally, the findings demonstrate that the analysis of startup stator current and speed signals presents the potential to detect distributed bearing damage in inverter-fed induction motors. Full article

Maintenance strategies such as condition-based maintenance and predictive maintenance of machines have gained importance in industrial automation firms as key concepts in Industry 4.0. As a result, online condition monitoring of electromechanical systems has become a crucial task in many industrial applications. Motor current signature analysis (MCSA) is an interesting noninvasive alternative to vibration analysis for the condition monitoring and fault diagnosis of mechanical systems driven by electric motors. The MCSA approach is based on the premise that faults in the mechanical load driven by the motor manifest as changes in the motor’s current behavior. This paper presents a novel data-driven, MCSA-based CM approach that exploits autoregressive (AR) spectral estimation. A multiresolution analysis of the raw motor currents is first performed using the discrete wavelet transform with Daubechies filters, enabling the separation of noise, disturbances, and variable torque effects from the current signals. AR spectral estimation is then applied to selected wavelet details to extract relevant features for fault diagnosis. In particular, a reference AR power spectral density (PSD) is estimated using data collected under healthy conditions. The AR PSD is then continuously or periodically updated with new data frames and compared to the reference PSD through the Symmetric Itakura–Saito spectral distance (SISSD). The SISSD, which serves as the health indicator, has proven capable of detecting fault occurrences through changes in the AR spectrum. The proposed procedure is tested on real data from two different scenarios: (i) an experimental in-house setup where data are collected during the execution of electric cam motion tasks (imbalance faults are emulated); (ii) the Korea Advanced Institute of Science and Technology testbed, whose data set is publicly available (bearing faults are considered). The results demonstrate the effectiveness of the method in both fault detection and isolation. In particular, the proposed health indicator exhibits strong detection capabilities, as its values under fault conditions exceed those under healthy conditions by one order of magnitude. 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

Rotating machines are vital for ensuring reliability, safety, and operational availability across various industrial sectors. Among the faults that can affect these machines, shaft misalignment is particularly critical due to its impact on other components connected to the shaft, making it a key focus for diagnostic systems. Misalignment can lead to significant energy losses, and therefore, early detection is crucial. Vibration analysis is an effective method for identifying misalignment at an early stage, enabling corrective actions before it negatively impacts equipment efficiency and energy consumption. To improve monitoring efficiency, it is essential that the diagnostic system is not only intelligent but also capable of operating in real-time. This study proposes a methodology for diagnosing shaft misalignment faults by combining wavelet transform for feature extraction and transfer learning for fault classification. The accuracy of the proposed soft real-time solution is validated through a comparison with other time-frequency transformation techniques and transfer learning networks. The methodology also includes an experimental procedure for simulating misalignment faults using a laser measurement tool. Additionally, the study evaluates the thermal impacts and vibration signature of each type of misalignment fault through multi-sensor monitoring, highlighting the effectiveness and robustness of the approach. First, wavelet transform is used to obtain a good representation of the signal in the time-frequency domain. This step allows for the extraction of key features from multi-sensor vibration signals. Then, the transfer learning network processes these features through its different layers to identify the faults and their severity. This combination provides an intelligent decision-support tool for diagnosing misalignment faults, enabling early detection and real-time monitoring. The proposed methodology is tested on two datasets: the first is a public dataset, while the second was created in the laboratory to simulate shaft misalignment using a laser alignment tool and to demonstrate the effect of this defect on other components through thermal imaging. The evaluation is carried out using various criteria to demonstrate the effectiveness of the methodology. The results highlight the potential of implementing the proposed soft real-time solution for diagnosing shaft misalignment faults. Full article

A novel piezoresistive cantilever microprobe (PCM) with an integrated electrothermal or piezoelectric actuator has been designed to replace current commercial PCMs, which require external actuators to perform contact-resonance imaging (CRI) of workpieces and avoid unwanted “forest of peaks” observed at large travel speed in the millimeter-per-second range. Initially, a PCM with integrated resistors for electrothermal actuation (ETA) was designed, built, and tested. Here, the ETA can be performed with a piezoresistive Wheatstone bridge, which converts mechanical strain into electrical signals by boron diffusion in order to simplify the production process. Moreover, a new substrate contact has been added in the new design for an AC voltage supply for the Wheatstone bridge to reduce parasitic signal influence via the EAM (Electromechanical Amplitude Modulation) in our homemade CRI system. Measurements on a bulk Al sample show the expected force dependence of the CR frequency. Meanwhile, fitting of the measured contact-resonance spectra was applied based on a Fano-type line shape to reveal the material-specific signature of a single harmonic resonator. However, noise is greatly increased with the bending mode and contact force increasing on viscoelastic samples. Then, to avoid unspecific peaks remaining in the spectra of soft samples, cantilevers with integrated piezoelectric actuators (PEAs) were designed. The numbers and positions of the actuators were optimized for specific CR vibration modes using analytical modeling of the cantilever bending based on the transfer-matrix method and Hertzian contact mechanics. To confirm the design of the PCM with a PEA, finite element analysis (FEA) of CR probing of a sample with a Young’s modulus of 10 GPa was performed. Close agreement was achieved by Fano-type line shape fitting of amplitude and phase of the first four vertical bending modes of the cantilever. As an important structure of the PCM with a PEA, the piezoresistive Wheatstone bridge had to have suitable doping parameters adapted to the boundary conditions of the manufacturing process of the newly designed PCM. Full article

Colored textiles are valuable physical evidence often found at crime scenes. Analysis of the chemical structure of textiles could be used to establish a connection between fabric found at a crime scene and suspect cloths. High-performance liquid chromatography (HPLC) and mass spectroscopy coupled HPLC are traditionally used for the identification of dyes in fabric. However, these techniques are invasive and destructive. A growing body of evidence indicates that near-infrared excitation (λ = 830 nm) Raman spectroscopy (NIeRS) could be used to probe the chemical signature of such colorants. At the same time, it remains unclear whether environmental factors, such as solar light could lower the accuracy of NIeRS-based identification of dyes in textiles. In this study, we exposed cotton fabric colored with six different dyes to light and investigated the extent to which colorants fade during seven weeks using NIeRS. We found a decrease in the intensities of all vibrational bands in the acquired spectra as the time of the exposition of fabric to light increased. Nevertheless, utilization of partial least-squared discriminant analysis (PLS-DA) enabled identification of the colorants at all eight weeks. These results indicate that the effect of light exposure should be strongly considered by forensic experts upon the NIeRS-based analysis of colored fabric. Full article

Bearings are the most vulnerable component in low-voltage induction motors from a maintenance standpoint. Vibration monitoring is the benchmark technique for identifying mechanical faults in rotating machinery, including the diagnosis of bearing defects. The study of different bearing fault phenomena under induction motor transient conditions offers interesting capabilities to enhance classic fault detection techniques. This study analyzes the low-frequency localized bearing fault signatures in both the inner and outer races during the start-up and steady-state operation of inverter-fed and line-started induction motors. For this aim, the classic vibration envelope spectrum technique is explored in the time–frequency domain by using a simple, resampling-free, Short Time Fourier Transform (STFT) and a band-pass filtering stage. The vibration data are acquired in the motor housing in the radial direction for different load points. In addition, two different localized defect sizes are considered to explore the influence of the defect width. The analysis of extracted low-frequency characteristic frequencies conducted in this study demonstrates the feasibility of detecting early-stage localized bearing defects in induction motors across various operating conditions and actuation modes. Full article