Abstract The application of guided waves to investigate commonly used plate shapes in the aerospace, mechanical, and civil industries is plates with bend shapes. This article investigates the interaction of fundamental Lamb waves with notches in bent plates, commonly found in aerospace, mechanical, and civil engineering applications. These areas are particularly susceptible to failure due to defects such as cracks and notches, which often manifest as semicircular corrosion patches or 90-deg notches. The presence of notches affects stress distribution, necessitating thorough analysis to prevent accidents. Accordingly, this article focuses on the interaction of fundamental Lamb waves through two types of notches that could be present inside a bent metal plate section. To explore this, a hybrid numerical framework is employed which combines semianalytical finite elements (SAFEs) with the finite element method (FEM). A bent plate section with various notch types is simulated using FEM, while SAFEs facilitate the definition of wave propagation through healthy regions of the plate. The study analyzes the scattering behavior of Lamb waves for different notch configurations and examines both fundamental modes over a specified frequency range. With a change in the interrogation signal parameters, there is a noticeable difference in the sensitivity of scattered waves with different notch types. Formulating a strategy for identifying and locating a notch inside a bent plate may need careful consideration of the important conclusions drawn. Understanding these interactions, the aim of the article is to enhance the integrity assessment of structural components subject to such defects.

Abstract This study focuses on the analysis of vibration-based signatures obtained from deep groove ball bearings with faults on the inner race. Various time−frequency-based methods are commonly used to diagnose faults in bearings. However, due to the non-self-adaptive nature of these methods and the nonlinear and nonstationary signals produced by the faults, mode decomposition techniques are seen as promising methods. This article presents a novel approach based on Nonlinear Mode Decomposition (NMD), which decomposes the complex signal into nonlinear modes. The data are taken from an online database of deep groove ball bearing with inner race faults of different sizes. These data are then subjected to NMD to extract nonlinear modes. Statistical parameters are applied to select a subset of significant nonlinear modes from the complete set. Finally, the Fast Fourier Transform is applied to the Hilbert Transform (HT) of the selected modes to see fault frequency and its higher harmonics resulting from nonlinearity. Additionally, the instantaneous frequency and instantaneous phase, two key parameters acquired from the HT, are also plotted for normal and faulty bearings, and the results are discussed in the article. The proposed method offers a valuable approach for accurately detecting and diagnosing deep groove ball-bearing faults.

Abstract This article reports the observation made during the compressive fracture process of cementitious materials which has been studied using nonlinear ultrasonic testing (NUT) and acoustic emission (AE) testing techniques. The generated higher harmonics were recorded at a specific time interval and normalised with respect to the peak amplitude of the fundamental wave. The AE based damage index (D) was compared with the normalized second harmonic amplitude (X). The X parameter decreased to a minimum at failure load where the AE based b-value also reached its minimum value. Another nonlinear ultrasonic technique namely Sideband Peak Count Index (SPC-I) showed a similar variation when correlated with X parameter. It is concluded that both techniques - the normalized second harmonic amplitude and the sideband peak count index can be used as an indicator of internal damage growth. A parallel may exist between AE based damage index (D) and normalized second harmonic amplitude (X) and SPC-I for the damage growth monitoring in cementitious materials. Since SPC-I technique is much easier to implement in comparison to the higher harmonic generation technique, it is concluded that the SPC-I can replace HHG for monitoring the damage growth in heterogeneous cementitious materials.

Abstract Accurate fatigue crack width estimation is crucial for aircraft safety, however, limited research exists on (i) the direct relationship between fatigue crack width and Lamb wave signatures and (ii) probabilistic artificial intelligence (AI) approach for automated analysis using acoustic emission waveforms. This paper presents a probabilistic deep learning approach for fatigue crack width estimation, employing an automated wavelet feature extractor and probabilistic Bayesian neural network. A dataset constituting the fatigue experiment on aluminum lap-joint specimens is considered, in which Lamb wave signals were recorded at several time instants for each specimen. Signals acquired from the piezo actuator-receiver sensor pairs are related to the optically measured surface crack length. The sensitive features are automatically extracted from the signals using decomposition techniques called maximal overlap discrete wavelet transform (MODWT). The extracted features are then mapped through the deep learning model, which incorporates Bayesian inference to account for both aleatoric as well as epistemic uncertainty, that provides outcomes in the form of providing probabilistic estimates of crack width with uncertainty quantification. Thus, employing an automated wavelet feature extractor (MODWT) on a dataset of fatigue experiments, the framework learns the relationship between Lamb wave signals and crack width. Validation on unseen in-situ data demonstrates the efficacy of the approach for practical implementation, paving the way for more reliable fatigue life prognosis.

Abstract The purpose of this research work is to establish the functionality of the novel ultrasonic non-destructive inspection system and accurate gauging of pipes and to locate and visualise flaws in the form of B-Scan cross-sectional view (front-view) of the pipe under test. In this paper, presents a custom-made perspex inspection head assembly integrated with a stand-alone, Li-ion battery-powered and IP67-grade water-immersible ultrasonic instrumentation and gauging system, which enables an efficient assessment of the condition and health of pipes in stringent environments. Extensive inspection was carried out on six samples of 12” Inner Diameter (ID) type carbon steel (CS) pipes with length of 500 mm and having machined wall thickness to simulate loss of wall-thicknesses from 10 % over a length 150 mm of pipe, using 5 MHz spherically focused transducers. Further inspection were carried out on a 12” CS pipe with four notches and four flat bottom holes (FBH) machined on the OD side. Identical flaws were also machined onto 12” CS pipe of total length of 700 mm containing water inside the pipe in flowing condition with water flow rate of 100 litres per minute (LPM). The test results demonstrate the effectiveness of the developed IP67 grade water-immersible ultrasonic pipe inspection and gauging instrumentation system for assessing the condition and health of long-length carbon steel pipes operating in harsh environments.

Abstract This study presents experimental work to analyze spur gear pair with geometrical and operating parameters. The spur gear pair accommodates the correction in tooth addendum, gear backlash, and linear tip-relief profile modification with three levels. As per Taguchi L9 orthogonal array, nine test spur gear pairs are precisely manufactured to analyze the gear dynamics. Other basic gear design parameters and operating parameters were held at a constant level. Root-mean-square (RMS) acceleration in the vertical direction is used to quantify the dynamic response of test gear pairs. Experimental data are analyzed by using the Taguchi method to investigate the rank of influencing parameters and the optimum level of parameters to minimize vibration response. Gear backlash, compared to tooth addendum and linear tip-relief tooth profile modification, emerges as the most influential parameter. The optimal combination is addendum 3.3 mm, backlash 0.05–0.075 mm, and linear tip-relief tooth profile modification, yielding the lowest vibration generation. Finally, the confirmation test was performed by using a simulation study to validate the experimental results. The RMS acceleration value of the simulation study is 0.070 g, which is approximately the lowest of the experimental response values. Similarly, 20 experiments were conducted with different speed and load combinations to check the effect of operating conditions on gear dynamics. From these experimental studies, it is observed that the rank of influencing parameters and optimum level of geometrical are varied with respect to operating conditions. It may be concluded that operating speed and loading conditions play very important roles in the design of a quiet gear system. The optimized performance of spur gear pairs may vary across different operating conditions. It indicates that typically optimized spur gear pair operated at one particular combination of load and speed may not show good performance at other operating conditions. Therefore, this study suggests that the realistic range of operating conditions should be considered while selecting suitable levels of geometrical parameters.

Abstract This study investigates the dynamics of roller bearings by utilizing the dimension theory technique to diagnose the bearing clearance faults of revolving machines. The generation of local defects in rotating machines is closely related to the clearance behaviour of the rotor-bearing system. A dynamic model of bearing with dimension theory by matrix method (DTMM) is developed for characteristics of bearing clearance considering the influence of local defects on the inner and outer bearings races. The characteristics of bearing internal radial clearance considering the impact of the defect on the bearing are analyzed. An experimental study has been performed under various operational conditions. The noisy signal is subsequently eliminated using the Modulation Signal Bispectrum (MSB). The efficiency and reliability of the stated approach are evaluated using a specialized bearing test and a run-to-failure fibrizer test. As a result, this technology offers a significant opportunity to execute more cost-effective maintenance work to eliminate the breakdown of machinery.

Abstract Debonding between stiffener and base plate is a very common type of damage in stiffened panels. Numerous efforts have been made for debonding assessment in the stiffened panel structure using guided wave-based techniques. However, the previous studies were limited to the detection of through-the-flange-width debonding (i.e., full debonding). This paper attempts to develop a methodology for the detection and assessment of early-stage debonding (i.e., partial debonding) in the stiffened panel using machine learning (ML) algorithms. An experimentally validated finite element (FE) simulation model is used to create an initial guided wave dataset containing several debonding scenarios. This dataset is processed through a data augmentation process, followed by feature extraction involving higher harmonics of guided waves. Thereafter, the extracted feature is compressed using a deep autoencoder model. The compressed feature is used for hyperparameter tuning, training, and testing of several supervised ML algorithms, and their performance in the identification of debonding zone and prediction of its size are analysed. Finally, the trained ML algorithms are tested with experimental data showing that the ML algorithms closely predicts the zones of debonding and their sizes. The proposed methodology is an advancement in debonding assessment, specifically addressing early-stage debonding in stiffened panels.

Abstract Bearing fatigue life is significantly influenced by bearing clearance. Vibration monitoring of bearing clearance deviations can efficiently reveal bearing wear and give sufficient lead time for maintenance. This study investigates the dynamics of roller bearings utilizing the dimension theory with the support vector machine (SVM) technique for diagnosing the bearing clearance faults of revolving machines. The generation of local defects in rotating machines is closely related to the clearance behavior of the rotor-bearing system. A dynamic model of bearing with dimension theory by matrix method with SVM is developed for characteristics of bearing clearance considering the influence of local defects on the inner and outer bearings races. The characteristics of bearing internal radial clearance considering the impact of the defect on the bearing are analyzed. An experimental study has been performed to capture the vibration signature of radial clearance for different speeds and radial loads of the rotor. The rotor-bearing system equations are numerically integrated, and the results are validated with experimental findings. The collective effects among the four parameters (radial load, speed, defect size, radial clearance) are investigated in detail for the rotor-bearing system. The noisy signal is subsequently eliminated using the modulation signal bispectrum (MSB), and the peaks of the MSB results are represented by the bearing clearance indicator. The efficiency and reliability of the stated approach are evaluated using a specialized bearing test and a run-to-failure sugar centrifuge test. The results suggest that the proposed approach can detect a change in bearing clearance up to 40 µm.

Abstract In this study, the influence of the orientation of embedded piezoelectric ceramic lead zirconium titanate (PZT) on the mechanical performance of hybrid fiber-reinforced (polypropylene and glass fiber) concrete beams was evaluated. The performance of concrete was evaluated under self-weight, followed by assessing the mechanical property using the electromechanical impedance (EMI) technique after optimization of M30 grade concrete with polypropylene fiber and glass fiber. PZT patches are embedded at different orientations, i.e., 0 deg, 45 deg, and 90 deg, with the axis of the structure for monitoring the hydration of the RC beam. The change in stiffness due to heat hydration in the concrete structure after 5, 7, 14, 21, and 28 days was observed by curing hybrid concrete beams and examining them after 5, 7, 14, 21, and 28 days. On the fifth day, beams were simply supported and allowed to deflect under their weight, and measurements of heat hydration in terms of conductance at frequencies ranging between 1 and 1000 kHz were done. Similarly, days 7, 14, 21, and 28 were done. Day 5 was considered the baseline. It is noted that the PZT sensor placed at an angle of 45 deg is the least effective in recording the incremental changes in hydration that occurred in the concrete beam. The highest quality results were obtained at 90 deg, which is further demonstrated by statistically quantifying the changes using the root-mean-square deviation (RMSD) percentage method and proves to be the most optimized orientation to obtain the stiffness of the hybrid reinforced beam in terms of conductance.

Abstract The dynamic characteristics of rolling element bearings are strongly related to their geometric and operating parameters, most importantly the bearing unbalance. Modern condition monitoring necessitates the use of intrinsic mode functions (IMFs) to diagnose unbalance bearing failure. This paper presents an Hilbert–Huang transform (HHT) method to diagnose the unbalanced rolling bearing faults of rotating machinery. To initially reduce the noise levels with slight signal distortion, the noises of the sample in normal and unbalanced fault states are measured and denoised using the wavelet threshold approach. The complex vibration signatures are decomposed into finite IMFs with ensemble empirical mode decomposition technique. Fast Fourier techniques (FFT) are employed to extract the vibration responses of bearings that are artificially damaged using electrochemical machining on a newly established test setup for rotor disc bearings. The similarities between the information-contained marginal Hilbert spectra can be used to diagnose rotating machinery bearing faults. The data marginal Hilbert spectra of Mahalanobis and cosine index are compared to determine the fault indicator index's similarity score. The HHT models simplicity enhanced the precision of diagnosis correlated to the results of the experiments with weak fault characteristic signals. The effectiveness of the proposed approach is evaluated with several theoretical models from the literature. The HHT approach is experimentally proven with unbalance diagnosis and capable of classifying marginal Hilbert spectra distribution. Because of its superior time-frequency characteristics and pattern identification of marginal Hilbert spectra and fault indicator indices, the newly stated HHT can process nonlinear, non-stationary, and even transient signals. The findings demonstrate that the suggested method is superior in terms of unbalance fault identification accuracy for monitoring the dynamic stability of industrial rotating machinery.

Abstract The versatile nature of ultrasound imaging finds applications in various fields. A point contact excitation and detection method is generally used for visualizing the acoustic waves in Lead Zirconate Titanate (PZT) ceramics. Such an excitation method with a delta pulse generates a broadband frequency spectrum and wide directional wave vector. The presence of noise in the ultrasonic signals severely degrades the resolution and image quality. Deep learning-based signal and image denoising have been demonstrated recently. This paper bench-marked and compared several state-of-the-art deep learning image denoising methods with the classical denoising methods. The best-performing deep learning models are observed to be performing at par or, in some cases, even better than the classical methods on ultrasonic images. We further demonstrate the effectiveness and versatility of the deep learning-based denoising model for the unexplored domain of ultrasound/ultrasonic data. We conclude with a discussion on selecting the best method for denoising ultrasonic images. The impact of this work may help ultrasound-based defects identification equipment manufacturers to adopt a deep learning-based denoising model for more wider and versatile use.

Abstract In this article, vibration-based fault diagnostics and response classification have been done for defective high-speed cylindrical bearing operating under unbalance rotor conditions. An experimental study has been performed to capture the vibration signature of faulty bearings in the time domain and for different speeds of the unbalanced rotor. Two-dimensional phase trajectories are generated by estimating the time delay and embedding dimension corresponding to vibration signatures. Qualitative analysis involves the implementation of a deep convolutional neural network (DCNN) utilizing the phase portraits as input to classify the nonlinear vibration responses. Comparison with the state-of-art classifiers such as artificial neural network (ANN), deep neural network (DNN), and k-nearest neighbor (KNN) is presented based on classification accuracy values. Thus, the values obtained are 61%, 67%, 72%, and 99% for ANN, DNN, KNN, and DCNN, respectively. Hence, the proposed intelligent classification model accurately identifies the dynamic behavior of bearing under unbalanced rotor conditions.

Abstract The small punch test (SPT) approach is a miniature specimen testing technique to estimate the preserved mechanical strength of an in-service component to check its fitness for service. The SPT results are summarized in form of force-specimen deflection, (F − u) and force-punch displacement, (F − v) response. There are many standards published in an attempt to define a universally accepted approach for SPT-aided mechanical characterization. However, it was recognized that such standards were not concerned to practice a consistent approach while SPT response measurement and strength estimation toward outlining proclaimed best-fitting correlations. This paper narrates limitations caused by known inconsistent practices and proposed comprehensive correlations for accurate strength estimation for metallic materials which are exposed to 100–2000 MPa strengths.

Abstract Steam turbine blades are the important components in power system shaft lines subjected to severe temperatures, leading to low/high cycle fatigue failures. The transient conditions occurring during startup and shutdown events generate alternative stresses causing the fracture at the blade roots. The present work deals with the effect of localized damage on the vibration characteristics and damage identification study in the last stage low-pressure (LP) steam turbine blade. Initially, free vibration studies and transient analysis of the last row LP blade section are conducted using the finite element model. A crack near the root region is modeled by a torsional spring, whose stiffness is expressed in terms of crack depth ratio. Effects of crack depth ratio and location near the roots on the natural frequencies and transient response amplitudes are studied in detail. The relationship between the damage parameters and blade frequencies is established through the backpropagation neural network model.

Abstract Honeycomb composite sandwich structures are extensively used for the manufacturing of many different components of aerospace, automobiles, wind turbine blades, and marine ship hull structures. Despite its widespread use and advantages, the honeycomb core is frequently damaged during production and operation, even if the damage is not visible on the face-sheet. In this study, an ultrasonic-guided wave (GW) propagation technique is utilized for robust and reliable nondestructive evaluation of a honeycomb composite sandwich panel (HCSP) in the presence of core crush damage. A 2D semi-analytical model was developed to understand the dispersion characteristics in the HCSP and to identify various modes of GW propagation in the signals. Extensive numerical simulations are carried out using abaqus to study the guided wave interaction with core crush damage. For this purpose, two numerical models were considered (a realistic model with both crushed core and cavity, and a simplified model that only comprises the cavity) and experimentally validated using a contact-type transducer. The presence of core crush damage in an HCSP increases the amplitude and group velocity of the primary antisymmetric mode, and this characteristic has been used for localization of the core crush region in the HCSP. Finally, a damage detection algorithm using signal difference coefficient is presented for successful localization of the core crush region within a square monitoring area. Unlike other studies reported in the literature, we demonstrate the utility of the simplified numerical model for studying GW interactions with core crush defect and experimentally validate the nondestructive evaluation (NDE) technique to localize core crush defect on an HCSP.

Abstract Misalignment is among the most common causes of vibrations in rotary machinery. Modern machinery is complicated and installing a sensor might be tricky at times. As a result, noncontact type sensors are critical in such situations. The present study investigates the influence of combinations between speed, load, and fault severity upon system vibration by employing acoustic sensor. Although acoustic sensor is used in angular fault diagnosis, however, this is the first attempt to combine the noncontact type of sensor and response surface methodology (RSM) to study the influence of misalignment upon system vibration and the factors that induce system vibrations in a misaligned rotor system. To investigate the effect of these interactions on system performance, RSM with root-mean-square (RMS) as a response factor is used. Design of experiments is used to prepare experiments, while analysis of variance (ANOVA) is used to analyze the results. Speed has a significant impact on RMS value in both parallel and angular types of misalignments and it severely affects the system's performance. According to the RSM findings, a change in load influences vibration amplitude. With increasing defect severity, the change in RMS value was not particularly significant. The outcome of RSM using acoustic sensor was found well aligned with the conclusion drawn using RSM study with vibrational sensor.

Abstract A guided wave-based method for localization of breathing delamination is presented in this investigation. The proposed technique utilizes one-way mixing of a dual-frequency fundamental antisymmetric Lamb modes with judiciously selected central frequencies. The dual-frequency interrogation signal, upon interacting with a breathing delamination, leads to additional frequency sidebands in the frequency response spectrum, strength of which is quantified in terms of the combination tone index. The numerical predictions of these sidebands are validated using an in-house experimentation. It is further exposited that the combination tone index depends strongly on the extent of the temporal overlap that the two constituent wave envelopes have as they propagate through the breathing delamination. Accordingly, for a synchronous passage (with 100% temporal overlap), the combination tone index is maximum while it reduces with the decreasing temporal overlap. By utilizing the dispersive nature of the chosen Lamb mode, a relation is then developed correlating the temporal separation of the wave envelopes at the location of the actuator, the group speeds, and the distance between the actuator and the delamination. Based on these inferences, a technique for localizing a breathing delamination is proposed, which involves interrogating the component by systematically altering the temporal overlap in the input waveform and monitoring the combination tone index for its maxima. The efficacy of the localization technique (close to 90%) is demonstrated through an illustrative case analyzed numerically as well as experimentally.

Abstract Rolling element bearings are one of the most common mechanical components used in a wide variety of rotating systems. The performance of these systems is closely associated with the health of bearings. In this study, a nonlinear time series analysis method, i.e., recurrence analysis is utilized to assess the health of bearings using time domain data. The recurrence analysis acquires the quantitative measures from the recurrence plots and provides an insight to the system under investigations. Experiments are performed to generate the vibration data from the healthy and faulty bearing. Eight recurrence quantitative analysis measures and five time-domain measures are used for the investigations. Three artificial intelligence techniques: rotation forest, artificial neural network, and support vector machine are employed to quantify the diagnosis performance. Results highlight the ability of recurrence analysis to identify the health state of the bearing at the early stage and superior diagnosis accuracy of the proposed methodology.

Abstract This paper investigates the applicability of the nondestructive testing and evaluation (NDT&E) method using ultrasonic signals to monitor the curing of alkali-activated fly ash-based concrete (AAFC). The evaluation was carried out on AAFC specimens with two different water/binder (W/B) ratios of 0.3 and 0.5 and after curing at 60 °C for 7, 14, and 28 days, respectively. The signals are recorded and analyzed using linear and non-linear ultrasonic techniques. The results show that the non-linear ultrasonic technique has a clear advantage over the linear ultrasonic technique when monitoring the curing of AAFC specimens with a lower W/B ratio. However, the specimens with the higher W/B ratio do not undergo proper curing and therefore do not show clear distinctions between the curing times measured from the two ultrasonic techniques. The unconfined compressive strength (UCS) of the AAFC specimens at different W/B ratios and curing times is also measured. The UCS results showed a good correlation with the ultrasonic results.

Abstract Misalignment is one of the key reasons for vibrations in most of the rotating system. The present study focuses on interactions among speed, load, and defect severity by investigating their effect on the system vibration. Response surface methodology (RSM) with root-mean-square (RMS) as a response factor is used to understand the influence of such interactions on the system performance. Experiments are planned using design of experiments, and analysis is carried out using analysis of variance (ANOVA). It is observed that speed has a remarkable effect on RMS value in both parallel and angular types of misalignment and affects the system performance. RSM results revealed that a change in load has less impact on vibration amplitude in case of horizontal and vertical directions, but there is a significant variation in RMS value in axial direction for both types of misalignment. A slight increase in the RMS value with an increase in defect severity is observed in the axial direction. These observations will help to understand the misalignment defect and its effect in a better way.

Abstract With the advent of industry 4.0, which conceptualizes self-monitoring of rotating machine parts by adopting techniques like data analytics, cloud computing, Internet of things, machine learning (ML), and artificial intelligence. The significant research area in predictive maintenance is tool condition monitoring (TCM) as the tool condition affects the overall machining process and its economics. Lately, machine learning techniques are being used to classify the tool’s condition in operation. These techniques are cost saving and help industries with adopting future-proof solutions for their operations. One such technique called discriminant analysis (DA) must be examined particularly for TCM. Owing to its less-expensive computation and shorter run times, using them in TCM will ensure the effective use of the cutting tool and reduce maintenance times. This article presents a Bayesian optimized discriminant analysis model to classify and monitor the tool condition into three user-defined classes. The data are collected using an in-house designed and developed data acquisition (DAQ) module setup on a Vertical Machining Center (VMC). The hyperparameter tuning has been incorporated using Bayesian optimization search, and the parameter that gives the best model was found out to be “linear,” achieving an accuracy of 93.3%. This study confirms the feasibility of machine learning techniques like DA in the field of TCM and using Bayesian optimization algorithms to fine-tune the model, making it industry ready.

Abstract Turbine blades are critical machine components in power plants and aerospace turbo engines. Failure of these blades in operation leads to catastrophic damages as well as high cost of maintenance and repair. Blades are often assembled in packets with lacing wire or shroud ring interconnections. Natural frequencies of the bladed packets are designed in a specific range to avoid possible resonant stresses. However, frequent damages during operation alter the stiffness of the blade-packet assembly and change the eigen-spectrum. A numerical study is presented in this work, where it is demonstrated that characteristic changes in eigen-spectrum can identify both severity and location of such damages. The work employs matrix perturbation theory on the eigen-value problem, formulated from the lumped-parameter modeling of the blade packet. Damage is considered as a perturbation in the stiffness matrix with damage severity acting as the perturbation parameter. First, a graphical pattern recognition method, and then, a damage proximity index evaluation method is suggested for damage identification. Further, an estimation algorithm for damage severity is presented with numerically simulated computations, which demonstrates that the methods can exactly identify the damage location and, with very little error, can estimate the damage severity.

Abstract Bearing remnant operational life can be determined by implementing a data-driven prognostics method. In this work, the bearing run-to-failure data from experimentation on test rig is used to extract time-domain features. The sudden change in time-domain information signifies the fault inception which led to failure stage promptly. The monotonicity metric is utilized to select the optimal feature set that best represents bearing degradation. Principal component analysis (PCA) is used for dimension reduction and fusion, and a unidimensional health indicator (HI) is constructed. Fluctuations of HI are smoothed by fitting it with a Weibull failure rate function (WFRF) and the corresponding parameters are estimated using nonlinear least-squares method. By inverting the model, the predicted time values are calculated, and hence remnant operational life of bearing is evaluated and compared with the actual life from experimental data. The performance assessment metrics utilized are mean absolute percentage error (MAPE), mean-square error (MSE), root-mean-square error (RMSE), and bias. Besides this, an online degradation state classification method using the k-nearest neighbor (KNN) classifier is implemented. The KNN model performance is assessed by constructing receiver operating characteristics (ROC) curve, which indicates the value of area under the curve (AUC) equal to 0.94, representing high accuracy of the KNN. The remaining useful life (RUL) is predicted within 95% confidence limits, and the predicted RUL almost follows the actual one with some fluctuations. The model performance is found promising and can be implemented to evaluate the remaining useful life of bearing.