Due to its simplicity, accuracy, and adaptability, Crimp Force Monitoring (CFM) has long been the standard for fault detection in wiring harness manufacturing. However, it necessitates frequent reconfigurations based on the variability in materials, dependency on operator skill, and high costs of implementation, and thus reconfiguration presents significant challenges. To solve these problems, this paper introduces a fault detection system that employs an Artificial Intelligence (AI) classification model to enhance the performance and cost-efficiency of the quality control process of wiring harness manufacturing. Since there are no labeled data to train the classification model at the onset of manufacturing, a small number of normal data from each production run are manually extracted to train the model. To address the constraint of the limited available data, the system generates synthetic data from normal data, simulating potential defects by using Regional Selective Data Scaling (RSDS). This innovative method performs upscaling or downscaling on specific regions of the original data to produce synthetic abnormal data, which enables the fault detection system to efficiently train its classification model with a dataset consisting solely of normal operation data.

The reliability of electric vehicles (EVs) is crucial for the performance and safety of modern transportation systems. Electric motors are the driving force in EVs, and their maintenance is critical for efficient EV performance. The conventional fault detection methods for motors often struggle with accurately capturing complex spatiotemporal vibration patterns. This paper proposes a recurrent convolutional neural network (RCNN) for effective defect detection in motors, taking advantage of the advances in deep learning techniques. The proposed approach applies long short-term memory (LSTM) layers to capture the temporal dynamics essential for fault detection and convolutional neural network layers to mine local features from the segmented vibration data. This hybrid method helps the model to learn complicated representations and correlations within the data, leading to improved fault detection. Model development and testing are conducted using a sizable dataset that includes various kinds of motor defects under differing operational scenarios. The results demonstrate that, in terms of fault detection accuracy, the proposed RCNN-based strategy performs better than the traditional fault detection techniques. The performance of the model is assessed under varying vibration data noise levels to further guarantee its effectiveness in practical applications.

Prognostics and health management (PHM) has developed into a crucial discipline because of its never-ending pursuit of safety, effectiveness, and dependability. The aircraft Landing gear (LG) is one of the most significant components during takeoff and landing. Consequently, the PHM of LG is essential for the aircraft to operate safely and reliably. This paper provides an in-depth exploration of the developments, difficulties, and prospects in PHM for aircraft LG. The study begins by providing an overview of the LG parts and related faults, emphasizing their importance for the flight safety. The insights of PHM are presented based on various artificial intelligence (AI) techniques. Various approaches are discussed for fault detection and isolation (FDI) and remaining useful life (RUL). These efforts help to improve the maintenance and decision-making (MDM) process, which improves the overall effectiveness of PHM. With the aim of giving researchers a useful resource, this review addresses to fill the research gaps based on the available literature so far. It lays the foundations for future advancements by highlighting the challenges in this field.

Prognostics and health management (PHM) is an enabling technology essential for the safe operation and conditional maintenance of any engineering system. Over the past two decades, the use of fiber-reinforced polymer (FRP) composites has increased to develop high-strength, lightweight, and durable composite structures. PHM technology has been widely adopted in the composites industry to address challenges such as damage detection, localization, remaining useful life (RUL) prediction, and maintenance scheduling. However, implementing PHM technologies for FRPs necessitates a thorough understanding of their failure modes and the non-destructive testing (NDT) techniques commonly used to assess their health condition. Therefore, this review provides a comprehensive discussion on the failure modes of FRP composites based on practical loading conditions, examines several NDT techniques employed on FRP composites, detailing their advantages and limitations, and highlights the current challenges while providing future directions for the use of NDT techniques in PHM of composite structures. Thus, the review aids practitioners, the scientific community, and new researchers in understanding the failure modes of FRP composites and exploring recent advancements in NDT techniques to address the current challenges and limitations in PHM of composite structures.

In consequence of their superior performance and durability, industrial robots have enjoyed widespread adoption across a variety of industries. However, despite their sturdy build, they are susceptible to malfunction. The servomotor is a fundamental component of industrial robots, and to ensure smooth and uninterrupted functioning, it is essential to detect any defects it may develop. Although research has addressed methods for detecting bearing failure, diagnosis of a servomotor bearing failure in the industrial robot remains difficult and requires intensive research. In this paper, a novel method for detecting servomotor bearing defects in the industrial robot is provided by integrating knowledge transfer via transfer learning. Initially, current signals of the servomotor are transformed to scalogram images. This processed data is utilized to build the model for fault detection. Applying transfer learning eliminates model training from scratch and streamlined operations. The purported approach shows an average accuracy of more than 99 %.

In recent development and improvement of wireless communication system, the cognitive radio (CR) is a potential approach to utilize spectrum efficiently. Spectrum sensing technique arguably is the most significant component of cognitive radio. Cooperative spectrum sensing (CSS) is utilized to improve the detection performance of the system. Several fusion strategies of decision making are presented for sensing the primary user but they do not perform well under low signal to noise ratio (SNR) conditions. This paper proposes artificial neural network (ANN) based CSS under Rayleigh multipath fading channel in IEEE 802.22 wireless regional area network (WRAN). We implemented an ANN in the fusion centre. First, the energy of the received signal is calculated using discrete wavelet packet transform (DWPT). Then, calculated energy, SNR and false alarm probability are used jointly to make a data set of 2048 samples and are used to train Levenberg-Marquardt back propagation training algorithm-based feed-forward neural network (FFNN). Using this trained neural network, CSS in WRAN is simulated under Rayleigh multipath fading channel and results demonstrate the superiority of the proposed approach over traditional CSS with DWPT and Fast fourier transform (FFT) based energy detection schemes in low SNR environment.

This paper introduces a novel approach to fault detection in the servo motor bearings of industrial robots within the context of Industry 4.0 prognostics and health management. The proposed solution leverages the innovative feature aggregation network for robotic fault detection in the application of smart factory. Overcoming challenges associated with traditional techniques that include handcrafted features, transfer learning, and deep learning models the proposed approach offers a hierarchical information aggregation mechanism. The model is customized through hyperparameter tuning, resulting in a streamlined architecture with significantly fewer parameters. This parameter efficiency is notably distinct when compared to off-the-shelf transfer learning models that commonly feature extensive parameter counts in the range of hundreds of thousands or millions. The proposed model subjected to rigorous validation across diverse experimental scenarios that affirm its adaptability and robust performance. The model showcases accuracy in fault detection under both simple and welding motion scenarios, while its generalization capabilities are demonstrated as it successfully predicts health states in welding motion, showcasing versatility and reliability across various operational scenarios.

Carbon fiber reinforced polymer (CFRP) composites have been continuously replacing conventional metallic materials due to their excellent material properties. The orthotropic nature of CFRP composites makes them vulnerable to various types of damage. Among these, delamination stands out as the most common and severe form of damage. Therefore, deep learning based structural health monitoring (SHM) which performs autonomous health monitoring from sensor data have gained wide attention for delamination detection of CFRP composites. However, limited training data often restricts the application of these models for autonomous health monitoring. Therefore, the present research proposes convolutional neural network (CNN)-based pre-trained transfer learning method using ResNetV2 (RNV2) model to solve the data scarcity problem. The use of RNV2 model eliminated the need for developing the model from scratch and only required fine-tuning on the target composites dataset. The target dataset contained multi-class wavelet-transformed vibrational data obtained from CFRP specimens. The efficacy of the proposed approach is determined using various evaluation metrics on unseen dataset. The results of the validation demonstrated that the pre-trained RNV2 model can effectively perform SHM of CFRP composites even under limited data conditions.

Hydraulic cylinders are typical actuators that are used in many industries, including manufacturing and construction machinery. Due to the wide application of cylinders, cylinder failures could increase maintenance costs, reduce productivity, and raise safety issues. Therefore, estimating and predicting the condition of cylinders is necessary for cost reduction and safety. This paper reviews various methods that have been proposed to estimate and predict cylinder failures. The paper first investigates the types of failures that can occur in cylinders and their causes. The failures include internal leakage, external leakage, and seal wear. The sensors used to identify each type of failure are then introduced. Since the failure information of the cylinder is implicitly embedded in the measured data, different diagnostics methods for isolating the failure information have been developed for each sensor. The diagnostic methods vary from traditional feature engineering to recent artificial intelligence-based methods. The prognostics that provide the remaining useful life of the cylinder are then reviewed. Finally, the paper discusses the challenges associated with the fault prognosis of hydraulic cylinders and future prospects.

The empirical application of polarization and depolarization current (PDC) measurement of transformers facilitates the extraction of critical insulation-sensitive parameters. This technique, rooted in time-domain dielectric response analysis, forms the bedrock for parameterization and insulation modeling. However, the inherently time-consuming nature of polarization current measurements renders them susceptible to data corruption. This article explores deep-learning-based short-duration techniques for forecasting polarization current to address this limitation. By incorporating spatial shortcuts, the residual long short-term memory (LSTM) network facilitates the seamless propagation of spatial and temporal gradients. Furthermore, the relative forecasting assessment of the proposed residual LSTM model’s performance is made against traditional LSTM, attention LSTM, gated recurrent units (GRU), and convolutional neural network (CNN) models. Thus, optimal model selection strategies are evaluated based on their capability to capture extended dependencies and short-term information present in the data. In addition, the Monte Carlo dropout prediction is employed to estimate uncertainty in polarization current forecasts. The findings demonstrate that the proposed residual LSTM network model for polarization current forecasting yields the lowest error metrics and maintains prediction consistency over the testing duration. Thus, the proposed approach significantly reduces PDC measurement time, providing an effective means to develop proactive maintenance strategies for evaluating the insulation condition of transformers.

The bearing is an indispensable part of mechanical systems. Fault diagnosis of bearing faults is vital for uninterrupted operations of the system, and to prevent catastrophic failure. Artificial intelligence implementation has revolutionized the bearing fault diagnosis method. Application of deep learning has eliminated manual feature extraction and selection requirements. While conventional convolutional neural networks have demonstrated potential in diagnosing faults, considering a more extensive variety of spatial variables can further optimize their performance. This paper proposes a multi-wide-kernel convolutional neural network-based model for bearing fault diagnosis. We propose wide kernels in the neural network's convolutional layers, which enable the model to learn broader patterns from the input for bearing fault diagnosis. The wide-kernel design enables the network to obtain local and global features more effectively, improving the network's capacity to distinguish between healthy and faulty bearings. We train and validate the proposed multi-wide-kernel convolutional neural networks using an extensive dataset of vibration signals collected from bearings under diverse scenarios. Because of its increased sensitivity to subtle fault patterns, the proposed model offers better accuracy. The model's efficacy is further confirmed by comparing it with existing cutting-edge techniques for diagnosing bearing faults.

At present, the fourth industrial revolution is pushing factories toward an intelligent, interconnected grid of machinery, communication systems, and computational resources. Smart factories (SF) and smart manufacturing (SM) incorporate a cyber-physical system that employs advanced technologies such as artificial intelligence (AI) for data analysis, automated process driving, and continuous data handling. Smart factories operate by combining machines, humans, and massive amounts of data into a single, digitally interconnected ecosystem. Prognostics and health management (PHM) has become a critical requirement of smart factories to meet pro-duction needs. PHM of components/machines in the smart factory is crucial for securing uninterrupted operation and ensuring safety standards. The growing availability of computational capacity has increased the use of deep learning in PHM strategies. Deep learning supports comprehensive PHM solutions, thus reducing the need for manual feature development. This review presents an extensive study of the PHM strategies employed in the smart factory ranging from the conventional perspective to the deep learning perspective. This includes consideration of the conventional methodologies used for health management along with latest trends in the PHM domain in the smart factory.

With increasing customer demand, industry 4.0 gained a lot of interest, which is based on smart factories. In smart factories, robotic components are vulnerable to failure due to various industrial operations such as assembly, manufacturing, and product handling. Timely fault detection and diagnosis (FDD) is important to keep the industrial operation smooth. Previously, only the unloaded-based FDD algorithms were considered for the industrial robotic system. In the industrial environment, the robot is working under various working conditions such as speeds, loads, and motions. Hence, to reduce the domain discrepancy between the lab scale and the real working environment, we conducted experimentations under various working conditions. For that purpose, an extensive experimental setup is prepared to perform a series of various experiments mimicking the real environmental condition. In addition, in previous research work, various machine learning (ML) and deep learning (DL) approaches were proposed for robotic arm component fault detection. However, various issues are related to the DL and ML approaches. The ML models are problem-specific, and complex in computations. The DL model needs a huge amount of data. The DL model is composed of various layers that have not been thoroughly explored; as a result, the fault detection model lacks a comprehensive explanation. To overcome these issues, the transfer learning (TL) model is considered with the diverse experimental scenarios. The main contribution is to increase the generalization capabilities of the robotic PHM in the context of previously available research work. For that purpose, the VGG16 model is used because of its autonomous feature extractions for fault classification. The data are collected under a variety of different operating conditions such as loadings, speeds, and motion patterns. The 1D signal is converted to a 2D signal (scalogram) to perform the TL model. The proposed approach shows effective fault detection performance and has the capabilities of generalization under variable working conditions.

Cracks are one of the forms of damage to concrete structures that debase the strength and durability of the building material and may pose a danger to the living being associated with it. Proper and regular diagnosis of concrete cracks is therefore necessary. Nowadays, for the more accurate identification and classification of cracks, various automated crack detection techniques are employed over a manual human inspection. Convolution Neural Network (CNN) has shown excellent performance in image processing. Thus, it is becoming the mainstream choice to replace the manual crack classification techniques, but this technique requires huge labeled data for training. Transfer learning is a strategy that tackles this issue by using pre-trained models. This work first time strives to classify concrete surface cracks by re-training of six pre-trained deep CNN models such as VGG-16, DenseNet-121, Inception-v3, ResNet-50, Xception, and InceptionResNet-v2 using transfer learning and comparing them with different metrics, such as Accuracy, Precision, Recall, F1-Score, Cohen Kappa, ROC AUC, and Error Rate in order to find the model with the best suitability. A dataset from two separate sources is considered for the re-training of pre-trained models, for the classification of cracks on concrete surfaces. Initially, the selective crack and non-crack images of the Mendeley dataset are considered, and later, a new dataset is used. As a result, the re-trained classifier of CNN models provides a consistent performance with an accuracy range of 0.95 to 0.99 on the first dataset and 0.85 to 0.98 on the new dataset. The results show that these CNN variants can produce the best outcome when finding cracks in the real situation and have strong generalization capabilities.

Coal mines are prone to fatal vulnerabilities due to improper airflow, a susceptible threat that leads to vitiating safety and human resources. Hence, continuous monitoring of the underground mine's airflow is essential for detecting any calamities. Various artificial intelligent methods estimate the underground mines' airflow (non-linear parameter). However, these methods fall into local minima and low convergence rates. This article proposed a novel algorithm that integrates an Adaptive Neural Fuzzy Interface System (ANFIS) and genetic algorithm (GA) to predict the energy consumption and airflow of the ventilation system for underground mines. A GA is studied to automatically search and configure network architecture to reduce the manual tuning effort required for optimal network architecture. Two predictive reference models (i.e., particle swarm optimisation (PSO) and Bayesian optimisation (BO)) are introduced for comparison to demonstrate the capability of GA in identifying the best hyper-parameters of ANFIS and ANN. To validate the proposed model, extensive experiment analysis and comparison with several baseline approaches in terms of the statistical parameters that include root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R2). In terms of the performance metric employed, the experiment findings indicate that the proposed model gives superior results over the baseline models. Thus, the proposed work advances the mine ventilation and monitoring system technologies to enhance performance and reliability, improve health and safety, reduce energy and operational cost, and enhance mine productivity.

Currently, many countries are investing significantly in the maintenance of civil infrastructures, viewing them as valuable assets at the national and international levels. Based on the recorded dynamic response, Structural Health Monitoring (SHM) is in a position to ensure the performance and safety of such structures. SHM is currently focusing on the usage of Wireless Sensor Networks (WSN) for damage detection because it has proven to be the best substitute for traditional visual inspection and wired/tethered sensor networks. In SHM, live tracking the structural response anytime anywhere is a predominant challenge. This work proposed and implemented an end-to-end live structural health monitoring framework based on the Internet of Things (IoT) to prevent the structure from collapsing unexpectedly. Furthermore, IoT assists in the live monitoring of structures at any time and from any location. Accelerometer sensors (40), signal conditioners (40), Data Acquisition Cards with an integrated Wi-Fi/Ethernet module (10), Access Point (1), and other components are included in the proposed framework. The accelerometer sensors are utilized to record the structural response, which is then analyzed by proposed Artificial Intelligence (AI) methods to identify the incipient damage and localize the damage. The AI methods are established using two datasets: one from a three-story building frame designed and realized in our laboratory, and the other from Los Alamos laboratory’s three-story bookshelf structure. It is a unique framework that offers three user interfaces (standalone, web-based, and mobile-based) to enable live monitoring locally and globally. The results demonstrate very good damage identification and localization accuracy, as well as the ability to live-monitor via interfaces.

The condition monitoring of squirrel cage induction motors (SCIMs) is vital for uninterrupted production and minimum downtime. Early fault detection can boost output with minimum effort. This article combines the application of transfer learning and convolution neural network (TL-CNN) for developing an efficient model for bearing and rotor broken bars damage identification in SCIMs. A simple technique for the 1-D current signal-to-image conversion is also proposed to provide input to the proposed deep learning-based TL-CNN technique. The proposed approach embodies the advantages of TL and CNN for effective fault identification in SCIMs. The developed technique has classified faults efficiently with an average accuracy of 99.40%. The complete analysis and data collection have been done on the experimental set-up with a 5 kW SCIM and LabVIEW-based data acquisition system. The propounded fault detection model has been created in python with the help of packages like Keras and TensorFlow.

In recent years, advancements in sustainable intelligent transportation have emphasized the significance of vehicle detection and tracking for real-time traffic flow management on the highways. However, the performance of existing methods based on deep learning is still a big challenge due to the different sizes of vehicles, occlusions, and other real-time traffic scenarios. To address the vehicle detection and tracking issues, an intelligent and effective scheme is proposed which detects vehicles by You Only Look Once (YOLOv5) with a speed of 140 FPS, and then, the Deep Simple Online and Real-time Tracking (Deep SORT) is integrated into the detection result to track and predict the position of the vehicles. In the first phase, YOLOv5 extracts the bounding box of the target vehicles, and in second phase, it is fed with the output of YOLOv5 to perform the tracking. Additionally, the Kalman filter and the Hungarian algorithm are employed to anticipate and track the final trajectory of the vehicles. To evaluate the effectiveness and performance of the proposed algorithm, simulations were carried out on the BDD100K and PASCAL datasets. The proposed algorithm surpasses the performance of existing deep learning-based methods, yielding superior results. Finally, the multi-vehicle detection and tracking process illustrated that the precision, recall, and mAP are 91.25%, 93.52%, and 92.18% in videos, respectively.

Health inspection of public structures is intended to detect incipient damage at an initial stage in order to improve maintenance. Artificial intelligence alludes to the part of computer science that comprises various techniques for fulfilling the requirements of Structural Health Monitoring (SHM). Deep Learning (DL), and Machine Learning (ML) are often utilized. Deep Learning is an instance of Machine Learning built on deep neural networks that have demonstrated remarkable achievement in numerous applications over the years. This article deals with recent literature reviews on the advent of machine learning models in the performance monitoring of civil structures. Recently, machine learning has gained considerable attention and is being built up as another class of astute techniques for the health inspection of civil structures. The main concern of this examination is to epitomize the strategies built over the last decade for the practice of ML techniques in civil engineering. In addition, types of sensors, number of sensors, sampling frequency, types of structure, structure material, data collection time, and types of excitation in the domain are also explored. Initially, a brief summary of the ML is given, and the implications of the ML in structural/civil engineering are depicted. Afterward, applications of ML methods in the domain are presented and the potential of these approaches to overcome the deficiencies of conventional methods is addressed. The observations after researching the literature, along with research opportunities and future directions in the use of ML, are then discussed. Eventually, a novel, secured framework for Structural Health Monitoring (SHM) using the Ethereum Blockchain is proposed established on the studies.