The main challenging goals in acoustic modality based moving vehicle recognition system is to accurately classify the moving vehicle with minimum misclassification rate. This article proposes an acoustic modality-based hybrid deep 1D convolutional neural network-bidirectional long short-term memory (CNN-BiLSTM) technique for moving vehicle classification under two-wheeler, low, medium, heavy weight category and noise. The proposed algorithm automatically extracts the high-level feature sequentially from experimentally generated vehicles signal and hold these features into the network for analyzing the time-varying characteristic for classification. Additionally, it is tested on the reference dataset SITEX02 for validation with 96% accuracy. The performance of 1D CNN-BiLSTM model has been compared with the conventional classifiers i.e., SVM, ANN, CNN and CNN-LSTM models. The experimental results show that CNN-BiLSTM has attained higher classification accuracy, 0.92 and minimum misclassification rate, 0.08 as compared to conventional classifiers. It has not only achieved satisfactory performance measures values and ROC-AUC characteristics but also obtained better generalization ability along with stability on the learning curve. 

Acoustic Modality Based Hybrid Deep 1D CNN-BiLSTM Algorithm for Moving Vehicle Classification

Moving target recognition with seismic sensing: A review

In the modern manufacturing industry, mechanical equipment plays a crucial role. Equipment working in harsh environments for a long time is more likely to break down, which can lead to economic losses or potential safety hazards. It is of significant importance to develop prognostic and health management technologies that correspond with the machines. Due to the non-stationarity of vibration signals in mechanical equipment, identifying fault features becomes challenging. Transforming the signals into images has been identified as a promising approach for fault diagnosis. This review provides an overview of recent data conversion imaging methods in the field of mechanical equipment. Additionally, it organizes the methods of feature enhancement and extraction based on these images. In the field of fault diagnosis for mechanical equipment, this review presents an overview of the framework used in intelligent fault diagnosis based on image-based techniques. With the development of artificial intelligence, image classification methods have more advantages compared to raw signal analysis. The purpose of this review is to provide readers with references on using image diagnosis methods for mechanical equipment. The significance of this review research is to provide readers with improved methods for image processing and image classification in the field of mechanical equipment. In addition, future development trends in this field were predicted, providing valuable insights and ideas for mechanical equipment fault diagnosis. 

Role of image feature enhancement in intelligent fault diagnosis for mechanical equipment: A review

The main challenging goals in acoustic modality based moving vehicle recognition system is to accurately classify the moving vehicle with minimum misclassification rate. This article proposes an acoustic modality-based hybrid deep 1D convolutional neural network-bidirectional long short-term memory (CNN-BiLSTM) technique for moving vehicle classification under two-wheeler, low, medium, heavy weight category and noise. The proposed algorithm automatically extracts the high-level feature sequentially from experimentally generated vehicles signal and hold these features into the network for analyzing the time-varying characteristic for classification. Additionally, it is tested on the reference dataset SITEX02 for validation with 96% accuracy. The performance of 1D CNN-BiLSTM model has been compared with the conventional classifiers i.e., SVM, ANN, CNN and CNN-LSTM models. The experimental results show that CNN-BiLSTM has attained higher classification accuracy, 0.92 and minimum misclassification rate, 0.08 as compared to conventional classifiers. It has not only achieved satisfactory performance measures values and ROC-AUC characteristics but also obtained better generalization ability along with stability on the learning curve.

Acoustic monitoring has recently shown great potential in the diagnosis of infrastructure condition. However, due to the severe noise interference in acoustic signals, meaningful features tend to be difficult to infer. It creates a considerable obstacle for an extensive application of acoustic monitoring. To tackle this problem, we propose an acceleration-guided acoustic signal denoising framework (AG-ASDF) based on learnable wavelet transform to automatically denoise the acoustic signal and extract the relevant features based on the acceleration signal. This denoising framework requires the acceleration signal only for the training stage. Therefore, only acoustic sensors (non-intrusive) need to be installed during the application phase, which is convenient and crucial for the condition monitoring of safety-critical infrastructure. A comparative study is conducted among the proposed AG-ASDF and other feature learning / extraction methods, by using a multi-class support vector machine to evaluate the detection effectiveness of slab track condition based on acoustic signals. Different healthy and unhealthy states of slab tracks are imitated with three types of slab track supporting conditions in a railway test line. The classification based on the proposed AG-ASDF features outperforms other feature extraction and learning methods with a significant accuracy improvement. 

/pdf/acceleration-guided-acoustic-signal-denoising-framework-2w0wq5ew.pdf

Acceleration-Guided Acoustic Signal Denoising Framework Based on Learnable Wavelet Transform Applied to Slab Track Condition Monitoring

In this paper, numerical simulations were carried out against reinforced concrete block by an ogive-nose-shaped projectile using ANSYS AUTODYN software A concrete block of size 1000 mm × 1000 mm × 1000 mm with mesh type of eight-node hexahedron solid elements is used as a target material The performance of the concrete block is modelled with RHT equation of state model, including both the damage and residual material strength In the present numerical simulations, 30 CRH Steel 4340 ogive-nose-shaped projectile with a mass of 265 kg is impacted against the reinforced concrete block by taking two cases of velocity In the first case, a linear velocity of 300 m/s is taken, whereas in second case linear as well as angular velocity is taken, while initial kinetic energy is the same for both the cases The simulations are performed using AUTODYN 3D software using Lagrange/Lagrange interaction technique to predict the penetration depth and crater diameter in the reinforced concrete block A deceleration profile is also plotted for the given projectile The numerical simulation results are in good agreement with analytic results

Numerical Simulation of Spinning Kinetic Energy Projectile Against Reinforced Concrete Target Using ANSYS AUTODYN

This paper introduces a time domain energy detector (ED) approach for vehicle detection using a single microphone. The detection of a deterministic, as well as the stochastic signal in the presence of white Gaussian noise (WGN), is a very challenging task. The ground vehicle generates a similar kind of signal with unknown information. Therefore, there is need of robust vehicle detection algorithm which must detect a vehicle with low false alarm. We proposed an algorithm based on energy detector and estimation of threshold value depending on noise's variance for moving vehicle detection using acoustics data. The energy distribution of the signal follows the chi-square distribution. These decision statistics reveal the vehicle presence information from the signal and detection probability of a true event. The algorithm is tested on the experimental data acquired from the field trials of the moving vehicle. The receiver operating characteristics are computed through Monte Carlo simulations for validation of the proposed algorithm. The results show that the algorithm is capable to detect the moving vehicle with low false alarm even at low signal to noise ratio.

Time Domain Energy Detector approach for Detection of Moving Ground Vehicle

This paper summarizes the application of numerical simulation and empirical approach to examine the penetration effect of an ogive-nose-shaped projectile against a concrete target. Many researchers were working on a concrete target as it is pertinent in structural applications such as bunkers, buildings, dams. Furthermore, reinforcement in concrete minimizes the scabbing and spalling of the target material. Thus, the concrete target is reinforced with steel bars of different grades and is tested by impacting projectiles by varying its nose shape. Our main focus was to compute the maximum depth of penetration and crater diameter in the concrete target material. Here, various numerical simulations are carried out for an ogive-nose-shaped projectile against a plain as well as reinforced concrete target at an impact velocity of 350 m/s. The simulations are performed using a commercial hydrocode ANSYS AUTODYN, a well-known software to compute transmission of shockwaves as a utility of time, and their results are compared with empirical equations. Lagrange interaction is applied between projectile and target material. The outcomes which are acquired from the simulations express upright promise with the empirical calculations.

Numerical Simulation and Parametric Study of Penetration Effect for an Ogive-Nose-Shaped Projectiles Against Concrete Target

This paper introduces a time-frequency spectrum sensing (TFSS) approach based on the short-time Fourier transform (STFT) for vehicle detection using a single acoustic sensor. Spectrum sensing on time-frequency plane offers various applications including vehicle detection, tracking, and classification. The proposed technique estimates the threshold from the power spectral density (PSD) distribution of white Gaussian noise (WGN) which is fitted with Chi-square distribution. This threshold is further used to extract the dominating frequency harmonics and detect the presence of a vehicle from the PSD of the signal on the spectrogram plane. Various performance measures are computed on experimentally generated datasets of the moving vehicles for evaluation of the classical amplitude-based energy detector (ED) and proposed algorithm. The main challenging issue for vehicle monitoring system is, to detect the moving vehicle with a minimum probability of false alarm rate at a low signal-to-noise ratio (SNR). The TFSS can detect the signal at a low SNR level up to −11dB with reduced false alarm rate by 50% as compared to ED. It also provides satisfactory performance measures and receiver operating characteristic curve.

Single Acoustic Sensor-Based Time–Frequency Spectrum Sensing Approach for Land Vehicle Detection

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Papers

Numerical Simulation of Spinning Kinetic Energy Projectile Against Reinforced Concrete Target Using ANSYS AUTODYN

Time Domain Energy Detector approach for Detection of Moving Ground Vehicle

Numerical Simulation and Parametric Study of Penetration Effect for an Ogive-Nose-Shaped Projectiles Against Concrete Target

Single Acoustic Sensor-Based Time–Frequency Spectrum Sensing Approach for Land Vehicle Detection