Vibration Response Signals Research Articles

Bridge modal identification method based on adaptive VMD-HT algorithm

ABSTRACT Bridge engineering structures play a crucial role in transportation. Traditional methods of road and bridge closures for inspection and evaluation, while effective, increase bridge inspection costs and disrupt normal traffic flow. Therefore, a rapid detection and evaluation method for bridge structural modal parameters in the operational state is more suitable for practical engineering applications. However, under real operating conditions, bridge vibration signals typically contain a large amount of noise and interference, and modal identification must overcome issues of non-stationarity and non-linearity in the vibration signals caused by vehicle loads and environmental changes to ensure the accuracy of the identification. This study replaces the traditional Empirical Mode Decomposition algorithm in the Hilbert-Huang Transform with the Variational Mode Decomposition algorithm. It proposes a new method that combines the VMD algorithm with the Hilbert Transform, referred to as the Variational Mode Decomposition-Hilbert Transform method. Compared to the EMD algorithm, it offers significant improvements in suppressing mode mixing and endpoint effects. A vehicle-bridge coupling model was constructed, and the bridge’s natural modal information was successfully extracted from the vibration response signals. The research results indicate that the improved algorithm offers higher accuracy and faster computational speed compared to traditional identification algorithms.

Analysis of Vibration Characteristics of Bridge Structures under Seismic Excitation.

Bridges may undergo structural vibration responses when exposed to seismic waves. An analysis of structural vibration characteristics is essential for evaluating the safety and stability of a bridge. In this paper, a signal time-frequency feature extraction method (NTFT-ESVD) integrating standard time-frequency transformation, singular value decomposition, and information entropy is proposed to analyze the vibration characteristics of structures under seismic excitation. First, the experiment simulates the response signal of the structure when exposed to seismic waves. The results of the time-frequency analysis indicate a maximum relative error of only 1% in frequency detection, and the maximum relative errors in amplitude and time parameters are 5.9% and 6%, respectively. These simulation results demonstrate the reliability of the NTFT-ESVD method in extracting the time-frequency characteristics of the signal and its suitability for analyzing the seismic response of the structure. Then, a real seismic wave event of the Su-Tong Yangtze River Bridge during the Hengchun earthquake in Taiwan (2006) is analyzed. The results show that the seismic waves only have a short-term impact on the bridge, with the maximum amplitude of the vibration response no greater than 1 cm, and the maximum vibration frequency no greater than 0.2 Hz in the three-dimensional direction, indicating that the earthquake in Hengchun will not have any serious impact on the stability and security of the Su-Tong Yangtze River Bridge. Additionally, the reliability of determining the arrival time of seismic waves by extracting the time-frequency information from structural vibration response signals is validated by comparing it with results from seismic stations (SSE/WHN/QZN) at similar epicenter distances published by the USGS. The results of the case study show that the combination of dynamic GNSS monitoring technology and time-frequency analysis can be used to analyze the impact of seismic waves on the bridge, which is of great help to the manager in assessing structural seismic damage.

Detection of jelly orange granulation disease using a dual-input Resnet-Transformer model (DresT) based on acoustic vibration images and a novel acoustic vibration device

Granulation is a common internal disease in citrus fruits, and it is difficult to distinguish fruits with granulation disease from their appearance. In this study, a novel acoustic vibration device based on a micro-LDV, a microphone and a resonance speaker was employed to collect acoustic vibration response signals of "Aiyuan 38" jelly orange. The one-dimensional acoustic vibration response signal was converted into acoustic vibration images, and a double-input Resnet-Transformer network (DresT) was constructed for extracting deep features in acoustic vibration images for identifying jelly-orange granulation disease. Firstly, train Drest and Resnet50 models using acoustic vibration images and compare the performance of Drest with that of Resnet50 (based on CNN). Then PLS-DA and SVM models are trained using acoustic vibration image texture features or acoustic vibration spectral features, and the performance is compared with the DresT model. The results showed that the DresT model trained using acoustic vibration images can accurately identify jelly orange granulation disease with a detection accuracy of 99.31 %. The F1 of the model is 99.5 %, the accuracy is 99.01 %, and the recall is 100 %.

Dynamic Load Identification at Natural Frequencies for Aircraft via Attention Based 1D-CNN

Since the frequency response function is ill-conditioned at the natural frequency of the system, the traditional load identification method based on the system parameters is no longer applicable. Aiming at the difficulty of dynamic load identification at the natural frequency of the structural system, a dynamic load identification method at the natural frequency of the structure based on one-dimensional convolutional neural network(1D-CNN) with attention mechanism is proposed. Specifically, the high-level features in the vibration response signal are first extracted through the convolution layer. Then the weight matrix of the network is updated by backpropagation algorithm, which represents the importance of different features. The mapping relationship between response and load is established to realize the task of load identification. From the trained data, the attention module learns the contribution of features according to the different contribution of different features to load prediction. The important components in the response signal are highlighted and noise pollution is suppressed. Excitation and response signals at the natural frequency of the system were acquired using exciters and an accelerometer mounted on the GARTEUR aircraft model. Excitation and response signals at the natural frequencies of the system are obtained by an exciter and accelerometer mounted on the GARTEUR aircraft model. The responses of the model at the first three natural frequencies of 6.4Hz,35.8Hz and 48.5Hz were obtained respectively. Experimental results show that compared with the traditional TSVD load identification method, the maximum error of this method is only 3.19%. Compared with the 1D-CNN method, the proposed method has stronger robustness under 20%, 50% and 80% noise levels.

Modal frequency identification using the predictor subspace approach for a space solar power station

The system’s modal frequency parameters are determined by using a technique known as predictor-based subspace identification (PBSID), which is based on the dynamic properties of space solar power stations’ huge size and low frequency. First, the dynamic equation for the space solar power station with an Abacus configuration’s attitude-vibration coupling is developed. Additionally, the PBSID approach is used to build the relevant parameter matrix, and singular value decomposition (SVD) is chosen to evaluate the system’s structural frequency parameters. The right input signals are created via numerical simulation, and the best sensor placement technique yields the associated vibration response signals. The computing results then demonstrate that the modal frequency characteristics of the space solar power station may be successfully identified by the PBSID method, which is based on SVD. Additionally, the findings demonstrate the superior noise immunity capabilities of the PBSID algorithm when compared to the standard modal parameter identification approaches.

Optimal tuning of three deep learning methods with signal processing and anomaly detection for multi-class damage detection of a large-scale bridge

Long-span bridges play a crucial role in urbanization, connecting communities across vast obstacles. Structural health monitoring techniques have been deployed on these bridges, generate large amounts of data through sensor measurements, requiring data-driven approaches like deep learning (DL) for effective analysis. However, feature extraction from time-domain vibration response signals poses challenges for DL methods. To address this, the study proposes utilizing signal processing techniques such as the multivariate empirical mode decomposition (MEMD) and Wavelet transform (WT) to extract essential features for damage classification. The incorporation of MEMD and WT aims to overcome limitations and process nonstationary and nonlinear signals effectively. Three DL techniques, long-short-term memory (LSTM), one dimensional convolutional neural network (1D-CNN), multi-layer perceptron (MLP) are tuned and applied to Structural Health Monitoring of Tianjin Yonghe Bridge (located in China) as a real-world case study, in order to detect its condition by Deep signal anomaly detection and identify types of the damage. A powerful meta-heuristic algorithm called Observer-Teacher-Learner-Based Optimization, is used to optimize both hyperparameters and architecture of each DL models. The results demonstrate that the optimally tuned DLs are successful in identifying types of damage, as well as the condition of the structure, for the Tianjin Yonghe Bridge. The average accuracy values are obtained as 98.13, 97.96, and 97.79 for 1D-CNN, LSTM, and MLP, respectively. Such optimally tuned DLs are evaluated as effective solutions for detecting damage on large-scale bridges by extracting statistical time-domain and time–frequency domain features using the WT and MEMD.

Road vehicle shock detection algorithm using the Hilbert envelope

Detecting and characterising shocks is challenging because they do not occur in isolation but are instead superimposed onto underlying vehicle vibrations which themselves are a result of the interaction with uneven road surfaces. Consequently, shocks are buried within vehicle vibration response measurements (usually acceleration). This paper presents the development and validation of an automated algorithm to detect shocks from vertical acceleration signals measured from road vehicles. To avoid inherent difficulties with experimentation, this initial paper is confined to numerical simulation whereby the response of two typical quarter-car truck models (one with air ride and the other with steel suspension) when travelling on artificially-generated random road elevation profiles laced with Hanning-shaped surface aberrations of known amplitude, lengths and location along the elevation profile. The shock detection algorithm was developed as a dual mode classifier to accommodate the two natural frequencies of each 2DoF quarter car models. Detection was implemented by first passing the vibration response signal through a band-pass filter around the two resonant modes in turn then calculating the filtered signals’ instantaneous frequency (envelope) by means of the Hilbert transform. Shock detection was based on the local peak-to-mean ratio (LPTMR) of the instantaneous magnitude where ‘local’ was defined by the duration of the filtered signal's impulse response function. Sensitivity analysis and validation were undertaken on artificially-generated roads of varying roughness onto which aberrations of known shape were superimposed. The effectiveness and limitations (detection threshold) of the algorithm were evaluated by creating a range of aberrations with a broad range of lengths (effective frequencies) and diminishing amplitudes. Results show that shocks of ‘significant’ magnitudes are always detected with no detection of false positives. As the road roughness increases relative to the aberration amplitudes, the resulting shocks become increasingly drowned-out by the vibration response due to the underlying road roughness, especially for the quarter-car model with steel suspension. The main conclusion is that the signal analysis approach taken is ultimately effective and needs to be further validated using experimental response data.

Speech extraction from vibration signals based on deep learning.

Extracting speech information from vibration response signals is a typical system identification problem, and the traditional method is too sensitive to deviations such as model parameters, noise, boundary conditions, and position. A method was proposed to obtain speech signals by collecting vibration signals of vibroacoustic systems for deep learning training in the work. The vibroacoustic coupling finite element model was first established with the voice signal as the excitation source. The vibration acceleration signals of the vibration response point were used as the training set to extract its spectral characteristics. Training was performed by two types of networks: fully connected, and convolutional. And it is found that the Fully Connected network prediction model has faster Rate of convergence and better quality of extracted speech. The amplitude spectra of the output speech signals (network output) and the phase of the vibration signals were used to convert extracted speech signals back to the time domain during the test set. The simulation results showed that the positions of the vibration response points had little effect on the quality of speech recognition, and good speech extraction quality can be obtained. The noises of the speech signals posed a greater influence on the speech extraction quality than the noises of the vibration signals. Extracted speech quality was poor when both had large noises. This method was robust to the position deviation of vibration responses during training and testing. The smaller the structural flexibility, the better the speech extraction quality. The quality of speech extraction was reduced in a trained system as the mass of node increased in the test set, but with negligible differences. Changes in boundary conditions did not significantly affect extracted speech quality. The speech extraction model proposed in the work has good robustness to position deviations, quality deviations, and boundary conditions.

Vibration-based looseness identification of bolted structures via quasi-analytic wavelet packet and optimized large margin distribution machine

Bolted joints are the most widely utilized connection types in industries, and therein looseness identification of bolted structures is of great significance to guarantee structural reliability. In this article, a comprehensive study of bolt looseness identification under random excitation is presented. To fulfill this task, this research focuses on three prominent difficulties, including nonstationary signal processing, subtle feature extraction, and robust state classification. First, a novel filter bank structure of quasi-analytic dual-tree complex wavelet packet transform is constructed to analyze the measured vibration response signals, for purpose of capturing subtle feature information. Then, multiple features are extracted from subband signals to capture the variations of dynamic characteristics, and sensitive features are selected by Laplacian score to construct the low-dimensional feature set. Subsequently, a novel classifier with better generalization performance, named large margin distribution machine, is optimized with the wavelet kernel function and the whale optimization algorithm, in order to handle the intrinsic uncertainty related to the looseness states of bolted structures. After feeding the low-dimensional feature set, the proposed classifier is trained to identify looseness states of bolted structures. Finally, experiments of a two-bolt lapped beam under random excitation are conducted to verify the effectiveness of the proposed method, and two typical loading conditions (paired-bolt looseness and single-bolt looseness) are considered. Besides, the superiority of the proposed method is demonstrated by comparing with other analogical methods. This research can provide a promising implement in practical applications of bolt looseness identification under random excitation.

Vibration response mechanism of fixed-shaft gear train with cracks based on rigid-flexible coupling dynamics and signal convolution model

Affected by external flexible transfer path of fixed-shaft gear train, internal gear meshing vibration signals that representing the healthy state of gear will convolve with modal information of gearbox housing and become much more complicated. Moreover, gear crack fault features of fixed-shaft gear train are relatively weak and complex, which are difficult to diagnose. Therefore, to reveal the origination mechanism of crack fault, a rigid-flexible coupling dynamics model of fixed-shaft gear train with full consideration of flexible housing is firstly established, then a signal convolution model is proposed to solve housing vibration response signal by combining internal excitation and external transfer path. Thereinto, as internal influence factor, time-varying meshing stiffness that drives excitation forces is used to reflect excitation characteristics under normal and crack state; as external influence factor, flexible transfer path that includes modal characteristics of gearbox housing is calculated by finite-element-method. Through the established models, modulation sideband features of housing vibration response signals are selected as crack fault indicator. Corresponding vibration response mechanism is revealed clearly under normal and different crack degrees in simulations. Experimental results keep good consistency at crack fault features with simulation signals and verify the effectiveness of the proposed models.

Operating modal analysis from sub-sampled vibration response based on compressed sensing and FastICA

In order to identify more high order modal from a small amount of vibration response signal sampling, this paper proposes a method for identifying the OMA of sub-sampled vibration response signals based on compression sensing (CS) and fast independent component analysis (FastICA). Firstly, the method subsamples a large number of vibrational signals to retain the original key information of the signal; Secondly, the subsampled signal is restored by using the compressive sampling matching pursuit (CoSaMP) algorithm of the compression-aware reconstruction algorithm. Finally, the source signal is separated by FastICA to obtain the multi-order natural frequencies and modal mode shapes of the structure. According to Shannon’s theorem, if the analog signal is reproduced without distortion, the sampling frequency should be no less than 2 times the highest frequency in the analog signal spectrum. This method can reduce the amount of original signal acquisition and the requirements for the frequency of the sampled signal, and the recovery signal has a high similarity with the original signal and the natural frequency is not changed. It can effectively identify the higher-order modes of the structure on the basis of breaking through the Nyquist sampling frequency. Experimental results of the subsampled vibration response signal on the uniform steel cantilever beam show that the method can reconstruct the original signal from a small number of subsampled vibration response signals, and identify the high-order nature frequency and modal mode shape that break through the Nyquist sampling frequency.

Working Mode Identification Method for High Arch Dam Discharge Structure Based on Improved Wavelet Threshold–EMD and RDT Algorithm

Prototype vibration response data of high arch dam discharge structures inevitably mix various noises under the discharge excitation, which adversely affects the accuracy of the working modal identification of the structure. To effectively filter noise and reduce modal aliasing for better identification accuracy, this study proposes an improved modal threshold identification method based on an improved wavelet threshold–empirical mode decomposition (EMD) and random decrement technique (RDT) algorithm for high arch dam discharge structures. On the basis of the measured vibration response data of the dam, the wavelet threshold is adopted to filter out most of the high-frequency white noise and to reduce the boundary accumulation effect of EMD decomposition. Detrended fluctuation analysis (DFA) is utilized to filter white noise and low-frequency flow noise after EMD decomposition. The natural frequency and damping ratio of the structure system are obtained by the improved RDT algorithm. The engineering examples show that the proposed method can accurately filter the measured vibration response signal noise of the discharge structure, retain the signal characteristic information, improve the accuracy of working modal recognition of the structural vibration response, avoid the complex ordering process of the system, and ease the working modal parameter identification of high arch dam discharge structures. This method can be applied to the mode identification of other large structures, as well.

Application of multi-base fusion generalized chirplet basis transform in vibration signal analysis of multiple rotor rotating machinery

In rotating machineries, there are many signals with multiple nonproportional fundamental frequencies such as the vibration response signal from a dual rotor aeroengine, where the speeds of the high-pressure turbine and the low-pressure turbine are not synchronized. The conventional Chirplet transform (CT) based time–frequency analysis (TFA) methods are capable of processing nonstationary signals mixed with multiple synchronous components, or proportional frequency components, but not suitable for processing the nonstationary signals with multiple nonproportional fundamental frequencies. This paper presents a novel nonstationary signal TFA method, referred to as General Chirplet Basis Transform (GCBT), which is designed for processing nonstationary signals with multiple nonproportional fundamental frequencies. In the proposed GCBT, the local maximum value search method is utilized to find out all the Chirplet basis function parameters matched with the fundamental frequencies of each harmonic group contained in the signal. Then, the time–frequency representation (TFR) fusion criterion is established to effectively combine the TFA results of all the discovered Chirplet basis functions. The effectiveness and superiority of the GCBT are demonstrated by analyzing a simulation signal, the vibration signals collected in the laboratory using two independently operated small rotors, and the vibration signals from an aeroengine test. All the results indicate that the proposed GCBT has the ability to handle multi-component signals with nonproportional fundamental frequencies, synchronous frequencies, closely-spaced frequencies, as well as the signals with sever noise backgrounds.

Dynamic Response of HTS Pinning Maglev System Under High Frequency Excitation

The full-size high temperature superconducting (HTS) pinning magnetic levitation (maglev) prototype train was launched in SWJTU, Chengdu, China in 2021. This prototype has shown the basic load and low speed operation capacity of HTS pinning maglev train. Hence, the next important issue should be its dynamical performance under high-speed. Moreover, in the same track condition, higher speed usually brings higher frequency excitation, which becomes the major vibration cause of the HTS pinning maglev system. Therefore, this paper will mainly focus on this point to study the dynamic response of the HTS pinning maglev system under high frequency. Initially, an experiment is designed to measure the vibration response signal under the external excitation in different directions. Secondly, the responses of the system in each direction under different excitation frequencies are compared. Finally, the vertical, lateral and coupling response of the system under high frequency in different field cooling height (FCH) are analyzed. The result verifies that the HTS pinning maglev can effectively isolate the high frequency vibration. And compared to the vertical excitation, the lateral excitations can affect the dynamic performance of the system to a greater extent. This study suggests the smoothness requirement of the operating line in high-speed, as well as providing references for future dynamic studies and design of HTS pinning maglev system.

Investigation on the characterisation of axle box resonance characteristics to wheel excitation

A growing number of studies have demonstrated the effectiveness and superiority of reverse engineering in using axle box vibration acceleration signals to deduce the operation state of wheelsets. However, there is a lack of discussion and summary on the overall mapping relationship between wheel excitation and axle box response, and there has been no detailed investigation on resonance characteristics of axle box. To address the deficiency, a novel resonant excitation loading method is proposed, which is applied to the vehicle model with flexible wheelset and axle box. Amplitude frequency response (AFR) curve of the resonance characteristics of axle box is obtained. AFR is used to characterise the characteristics of various excitation states of wheel, including wheel polygon, wheel tread flat, and wheel-rail separation state, and the corresponding evolution law is revealed. Finally, through two case studies, the guiding role of axle box resonance characteristics on wheel operation status detection is discussed in detail. Analyses show that mastering the resonance characteristics of axle box is an effective way to deduce the operation status of wheel based on the complex axle box vibration response signal, and it will provide a basis for the quantitative evaluation of wheel local damage.

Effects of operational variability and damage on structural response signals: A method based on LMS radar image and residual-permutation entropy

The study investigates the residual sequence from time series “black box” models and evaluates the nonlinear and non-stationary characteristics of the residual sequence using Lagrange Multiplier (LM) test. The Lagrange Multiplier Statistics (LMS) radar map is performed to analyze and clarify the corresponding relationship between the residual sequence and structural safety condition. Then, the permutation entropy is proposed to measure the complexity, randomness and fluctuation of the residual sequence, and the correlation is established between the residual-permutation entropy (R-PE), structural abnormal response signal and safety state. The effects of operational variability, damage, coupling disturbance of operational variability and damage on R-PE are analyzed in detail.In this paper, the proposed methodology is verified by vibration response signals of a three-storey shear-type frame in experiment and a six-storey shear-type frame in numerical simulation. The results indicate that i) LMS = 0.9n can be considered as the classification threshold for undamaged and damaged state of the structure using LMS radar map. ii) When the R-PE value is close to 1, it indicates that the structure is undamaged or not influenced by operational variability. iii) When the R-PE value is less than 1, it may be disturbed by operational factors, damage factors, or coupling disturbance of the two. iv) The R-PE value is very robust as the decrease of the R-PE value caused by operational disturbance is small, while the decrease of R-PE value caused by damage is significant. v) There is a significant difference of the R-PE value between small damage and operational impact on the structure.

Fatigue life prediction based on modified narrowband method under broadband random vibration loading

The vibration fatigue life and vibration response signal at the dangerous position of each specimen were obtained by the broadband random vibration fatigue test for 7075-T651 aluminum alloy, and the stress amplitude probability distribution of vibration response signal was counted by rainflow counting method. By comparing the probability density distribution functions of broadband random vibration signal by frequency-domain method and analyzing the fitting accuracy of different distribution models for rainflow distribution, a new model for frequency-domain fatigue life prediction based on modified narrowband distribution model was proposed. The proposed model takes the second-order spectral width parameter of vibration signal as the weight coefficient, and the life prediction results are more accurate and less dispersive.

The Improved WNOFRFs Feature Extraction Method and Its Application to Quantitative Diagnosis for Cracked Rotor Systems.

During its operation, a rotor system can be exposed to multiple faults, such as rub-impact, misalignment, cracks and unbalancing. When a crack fault occurs on the rotor shaft, the vibration response signals contain some nonlinear components that are considerably tougher to be extracted through some linear diagnosis methods. By combining the Nonlinear Output Frequency Response Functions weighted contribution rate (WNOFRFs) and Kullback–Leibler (KL) divergence, a novel fault diagnosis method of improved WNOFRFs is proposed. In this method, an index, improved optimal WNOFRFs (IOW), is defined to represent the nonlinearity of the faulty rotor system. This method has been tested through the finite element model of a cracked rotor system and then verified experimentally at the shaft crack detection test bench. The results from the simulation and experiment verified that the proposed method is applicable and effective for cracked rotor systems. The IOW indicator shows high sensitivity to crack faults and can comprehensively represent the nonlinear properties of the system. It can also quantitatively detect the crack fault, and the relationship between the values of IOW and the relative depth of the crack is approximately positively proportional. The proposed method can precisely and quantitatively diagnose crack faults in a rotor system.

Firmness measurement of kiwifruit using a self-designed device based on acoustic vibration technology

Firmness is an important quality indicator of kiwifruit and is useful for determining optimal time for marketing and optimizing storage management. In this study, a self-designed system based on acoustic vibration technology was used to realize real-time detection of kiwifruit firmness. To ensure stability of measurement, impact force of the excitation device in the system was calibrated to 12.03 ± 0.71 N. The acoustic vibration response signals of kiwifruit were converted from time domain to frequency domain and 10 statistical features were extracted. Most of the features had good correlations with reference firmness, which proved feasibility of firmness prediction using signals collected in the self-designed system. Subsequently, PLS regression models for predicting firmness were established based on frequency-domain spectra. To further improve accuracy of the model, CARS algorithm was used to select effective frequencies that were highly correlated with kiwifruit firmness. According to the results, prediction accuracy of the CARS-PLS model in external cross-validation sets for flesh firmness was the best (Rcv2 = 0.96, RMSECV = 0.27, and RPDcv = 5.21), followed by stiffness (Rcv2 = 0.95, RMSECV = 0.43, and RPDcv = 5.00), and prediction accuracy of the model for skin firmness was the worst (Rcv2 = 0.93, RMSECV = 0.81, and RPDcv = 4.01). Overall, acoustic vibration signals obtained by the self-designed device in a nondestructive way can well characterize the firmness of kiwifruit. The proposed method in this study can achieve high-precision prediction of all three kiwifruit firmness indices and meet requirements of online real-time detection.

Detection of apple firmness with a novel loudspeaker-based excitation device

Firmness is one of the important indices to evaluate the internal quality of fruit. In this study, a noncontact loudspeaker-based detection system was developed to evaluate apple firmness. The structural parameters of the excitation device were modified in the single-factor experiments, and the best combination of structural parameters was that the inner diameter of the gasket was 40 mm; the distance between fruit surface and loudspeaker was 95 mm. Besides, the proper posture style was that the apple was placed with its stem upward. After the modification of the Laser Doppler Vibrometer (LDV) method, the vibration response signals of 48 apples were measured to establish the firmness prediction model. The results showed that the better prediction performance of stiffness was obtained in multiple models. The Back Propagation Neural Network (BPNN) model had the best prediction performance by using parameters of elasticity index (EI), the peak value at the second resonance frequency f2(A2), and peak area S, with a correlation coefficient of prediction (rp) of 0.914; root mean square error of prediction (RMSEP) of 0.491 N/mm. Therefore, the proposed detection system is feasible to nondestructively detect apple firmness, which has the potential to be applied in online detection. Keywords: fruit firmness, excitation device, structural parameters, vibration parameters DOI: 10.25165/j.ijabe.20221501.7028 Citation: Ding C Q, Wang D C, Feng Z, Cui D. Detection of apple firmness with a novel loudspeaker-based excitation device. Int J Agric & Biol Eng, 2022; 15(1): 260–266.