Search Results (45)

With advancements in computational resources, artificial intelligence has gained significant attention in motor health monitoring. These sophisticated deep learning algorithms have been widely used for induction motor health monitoring due to their autonomous feature extraction abilities and end-to-end learning capabilities. However, in real-world scenarios, challenges such as limited labeled data and diverse operating conditions have led to the application of transfer learning for motor health monitoring. Transfer learning utilizes pretrained models to address new tasks with limited labeled data. Recent advancements in this domain have significantly improved fault diagnosis, condition monitoring, and the predictive maintenance of induction motors. This study reviews state-of-the-art transfer learning techniques, including domain adaptation, fine-tuning, and feature-based transfer for induction motor health monitoring. The key methodologies are analyzed, highlighting their contributions to improving fault detection, diagnosis, and prognosis in industrial applications. Additionally, emerging trends and future research directions are discussed to guide further advancements in this rapidly evolving field. Full article

A planetary gear fault generates periodic speed fluctuations, which significantly influence its vibration signal. It is a necessity to explore the vibration modulation features of gear faults to provide an effective indicator for fault detection. Therefore, a superior rigid-flexible coupling dynamics model of a planetary gearbox involving the fault-caused speed fluctuation is developed, where the meshing stiffness under the impact of fault-caused speed fluctuation is innovatively deduced utilizing the potential energy method; then, the meshing stiffness is substituted into the rigid dynamics model to calculate the excitation forces. Transfer path functions from excitation locations to the sensor installed on the housing are obtained by considering the modal parameters of the flexible housing. Finally, the excitation forces are combined with their transfer path functions to calculate the vibration signal. The fault modulation features of the cracked sun gear deduced by the superior dynamics model emerge surrounding the meshing frequency and its harmonics, as well as the resonance ranges, which can be a reliable sign for identifying faults. The experiment conducted on a single-stage planetary gearbox confirms the validity and superiority of the proposed model, which holds significant value for guiding fault detection and prognosis in planetary gearboxes. Full article

This review paper comprehensively analyzes the prognosis of rotating machines (RMs), focusing on mechanical-flaw and remaining-useful-life (RUL) estimation in industrial and renewable energy applications. It introduces common mechanical faults in rotating machinery, their causes, and their potential impacts on RM performance and longevity, particularly in wind, wave, and tidal energy systems, where reliability is crucial. The study outlines the primary procedures for RUL estimation, including data acquisition, health indicator (HI) construction, failure threshold (FT) determination, RUL estimation approaches, and evaluation metrics, through a detailed review of published work from the past six years. A detailed investigation of HI design using mechanical-signal-based, model-based, and artificial intelligence (AI)-based techniques is presented, emphasizing their relevance to condition monitoring and fault detection in offshore and hybrid renewable energy systems. The paper thoroughly explores the use of physics-based, data-driven, and hybrid models for prognosis. Additionally, the review delves into the application of advanced methods such as transfer learning and physics-informed neural networks for RUL estimation. The advantages and disadvantages of each method are discussed in detail, providing a foundation for optimizing condition-monitoring strategies. Finally, the paper identifies open challenges in prognostics of RMs and concludes with critical suggestions for future research to enhance the reliability of these technologies. Full article

Many industrial processes, from manufacturing to food processing, incorporate rotating elements as principal components in their production chain. Failure of these components often leads to costly downtime and potential safety risks, further emphasizing the importance of monitoring their health state. Vibration signal analysis is now a common approach for this purpose, as it provides useful information related to the dynamic behavior of machines. This research aimed to conduct a comprehensive examination of the current methodologies employed in the stages of vibration signal analysis, which encompass preprocessing, processing, and post-processing phases, ultimately leading to the application of Artificial Intelligence-based diagnostics and prognostics. An extensive search was conducted in various databases, including ScienceDirect, IEEE, MDPI, Springer, and Google Scholar, from 2020 to early 2024 following the PRISMA guidelines. Articles that aligned with at least one of the targeted topics cited above and provided unique methods and explicit results qualified for retention, while those that were redundant or did not meet the established inclusion criteria were excluded. Subsequently, 270 articles were selected from an initial pool of 338. The review results highlighted several deficiencies in the preprocessing step and the experimental validation, with implementation rates of 15.41% and 10.15%, respectively, in the selected prototype studies. Examination of the processing phase revealed that time scale decomposition methods have become essential for accurate analysis of vibration signals, as they facilitate the extraction of complex information that remains obscured in the original, undecomposed signals. Combining such methods with time–frequency analysis methods was shown to be an ideal combination for information extraction. In the context of fault detection, support vector machines (SVMs), convolutional neural networks (CNNs), Long Short-Term Memory (LSTM) networks, k-nearest neighbors (KNN), and random forests have been identified as the five most frequently employed algorithms. Meanwhile, transformer-based models are emerging as a promising venue for the prediction of RUL values, along with data transformation. Given the conclusions drawn, future researchers are urged to investigate the interpretability and integration of the diagnosis and prognosis models developed with the aim of applying them in real-time industrial contexts. Furthermore, there is a need for experimental studies to disclose the preprocessing details for datasets and the operational conditions of the machinery, thereby improving the data reproducibility. Another area that warrants further investigation is differentiation of the various types of fault information present in vibration signals obtained from bearings, as the defect information from the overall system is embedded within these signals. Full article

This paper comprehensively analyzes advanced Fault Diagnosis and Prognosis (FDP) techniques employed in aerial and space agents such as satellites, spacecraft, and Unmanned Aerial Vehicles (UAVs). The critical engineering functions of fault diagnostics and prognosis, particularly the emerging field of fault prognosis, emphasize the necessity for further advancement. Integrating these methodologies enriches the system’s capacity to diagnose faults in their early stages. Additionally, it enables the prediction of fault propagation and facilitates proactive maintenance to mitigate the risk of severe failure. This paper aims to introduce diverse FDP methods, followed by a discussion on their application and evolution within single and multisatellite/UAV systems. Throughout this review, eighty-five relevant works are analyzed and discussed and their evaluation metrics are expanded upon as well. Within the works analyzed in this review, it was found that data-driven methods constitute 54% and 7% of the methodologies utilized in single- and multiagent FDP, respectively, which underscores the rise of these methods in the field of single-agent FDP and their unexplored potential in multiagent condition monitoring. Finally, this review is brought to a close with a suggested classification scheme of the utilized methodologies in the field, a quantitative analysis of their contributions to the field, and remarks and mentions of the potential gaps in the area. Full article

Fault-finding diagnostics is a model-driven approach that identifies a system’s malfunctioning portion. It uses residual generators to identify faults, and various methods like isolation techniques and structural analysis are used. However, diagnostic equipment doesn’t measure the remaining signal-to-noise ratio. Residual selection identifies fault-detecting generators. Fault detective diagnostic (FDD) approaches have been investigated and implemented for various industrial processes. However, industrial operations make it difficult to implement FDD techniques. To bridge the gap between theoretical methodologies and implementations, hybrid approaches and intelligent procedures are needed. Future research should focus on improving fault prognosis, allowing for accurate prediction of process failures and avoiding safety hazards. Real-time and comprehensive FDD strategies should be implemented in the age of big data. Full article

The symmetry of vibration signals collected from healthy machinery, which gradually degenerates with the development of faults, must be detected for timely diagnosis and prognosis. However, conventional methods may miss spatiotemporal relationships, struggle with varying sampling rates, and lack adaptability to changing loads and conditions, affecting diagnostic accuracy. A novel bearing fault diagnosis approach is proposed to address these issues, which integrates the Gramian angular field (GAF) transformation with a parallel deep convolutional neural network (DCNN). The crux of this method lies in the preprocessing of input signals, where sampling rate normalization is employed to minimize the effects of varying sampling rates on diagnostic outcomes. Subsequently, the processed signals undergo GAF transformation, converting them into an image format that effectively represents their spatiotemporal relationships in a two-dimensional space. These images serve as inputs to the parallel DCNN, facilitating feature extraction and fault classification through deep learning techniques and leading to improved generalization capabilities on test data. The proposed method achieves an overall accuracy of 96.96%, even in the absence of training data within the test set. Discussions are also conducted to quantify the effects of sampling rate normalization and model structures on diagnostic accuracy. Full article

New trends in maintenance techniques are oriented to digitization and prognosis. The new electronic devices based on IoT (Internet of Things) technology among others that support the industry 4.0 paradigm let enhance the traditional condition monitoring techniques to better understand and predict the state of a machine in service. Related to maintenance applications, one of the important steps in condition monitoring tasks for fault diagnosis is the selection of the optimal pattern to provide accurate results (avoiding fault positives/negatives) with adequate computation time. When implementing this, the selection of optimal parameters and thresholds for setting alarms are important to detect problems in the machine before the failure occurs. Vibratory signals have been proved to be a good variable to determine their mechanical behavior. Nevertheless, parameters obtained from time domain measurements are not computationally efficient nor good patterns to compare different machine conditions. In this sense, tools that represent the frequency domain or time–frequency domain have been useful to detect defects in rotating elements such as bearings. In this work, defects in ball bearings are studied using wavelet packet transform. For this, a methodology will be developed for the optimal selection of the mother wavelet, incorporating intelligent classification systems, and using a medium Gaussian support vector machine model. In this way, it will be verified that the correct selection of this function influences both the results and the ease and reliability of detection. The results using the selected mother wavelet will be compared to those using Daubechies 6, since it is the mother wavelet that has been used in previous works and which was selected based on experience. For it, vibratory signals are obtained from a testbench with different bearing conditions: healthy bearings and defective bearings (inner and outer race). Full article

Decarbonization of the transport sector could be achieved through fuel cell technology. The candidature of this technology is motivated by its high current density and lack of emissions. However, its widespread deployment is restrained by durability and reliability constraints. During normal operation, the fuel cell system supplies stable power to the load. Contrarily, when it is operated under faulty conditions, the system’s output power deteriorates, leading to low durability. It is therefore of paramount importance to ensure that the system is operated in a non-faulty condition. In this paper, we provide a critical review of the analytical-redundancy-based fault diagnosis methods for proton exchange membrane fuel cells (PEMFCs). An in-depth analysis of the various methods has been presented in terms of accuracy, complexity, implementability, and robustness to aging and dynamic operating conditions. Full article

Acceleration sensors are tools for detecting acceleration and serve purposes like fault monitoring and behavior recognition. It is extensively employed in a variety of industries, including aerospace, artificial intelligence, biology, and many more. Among these, one of the major research hotspots and challenges is the development of low-energy, self-powered, miniature, mass-produced sensors. Due to its capacity to perceive human behavior and identify errors, the flexible acceleration sensor offers a distinct advantage in the use of flexible and miniaturized sensing systems. This review analyzes the current state of piezoelectric flexible acceleration sensors’ applications in the areas of sensitive materials, processing technology, and device structure and briefly summarizes the fundamental properties of these sensors. Additionally, it ends with a prognosis for the future growth of flexible piezoelectric acceleration sensors. Full article

In this paper, a comparison of signal analysis techniques for the diagnostics of rolling element bearings is carried out. Specifically, the comparison is performed in terms of fault detection, diagnosis and prognosis techniques with regards to the first rolling element bearing dataset released by NASA IMS Center in 2014. As for fault detection, it is obtained that RMS value, Kurtosis and Detectivity, as statistical parameters, are able to properly detect the arising of the fault on the defective bearings. Then, several signal processing techniques, such as deterministic/random signal separation, time-frequency and cyclostationary analyses are applied to perform fault diagnosis. Among these techniques, it is found that the combination of Cepstrum Pre-Whitening and Squared Envelope Spectrum, and Improved Envelope Spectrum, allow the faults to be correctly identified on specific bearing components. Finally, the Correlation, Monotonicity and Robustness of the previous statistical parameters are computed to identify the most accurate tools for bearing fault prognosis. Full article

The process of human exploration of the universe has accelerated, and aerospace technology has developed rapidly. The health management and prognosis guarantee of spacecraft systems has become an important basic technology. However, with thousands of telemetry data channels and massive data scales, spacecraft systems are increasingly complex. The anomaly detection that relied on simple threshold judgment and expert manual annotation in the past is no longer applicable. In addition, the particularity of the anomaly detection task leads to the lack of fault data for training. Therefore, a data-driven deep transfer learning-based approach is needed for rapid analysis and accurate detection of large-scale data. The control moment gyroscope (CMG) is a significant inertial actuator in the process of large-scale, long-life spacecraft in-orbit operation and mission execution. Its anomaly detection plays a major role in the prevention and elimination of early failures. Based on the research of SincNet and Long Short-Term Memory (LSTM) networks, this paper proposed a Sinc-LSTM neural network based on transfer learning and working condition classification for CMG anomaly detection. First, a two-stage pre-training method is proposed to alleviate the data imbalance, using the Mars Reconnaissance Orbiter (MRO) dataset and a satellite dataset from NASA. Second, the Sinc-LSTM network is designed to enhance the local fitting and long-period memory ability of the model for CMG time series data. Finally, a dynamic threshold judgment anomaly detection method based on working condition classification is designed to accommodate threshold changes for CMG full-cycle anomaly detection. The method is validated on the spacecraft CMG dataset. Full article

Robotic systems are a fundamental part of modern industrial development. In this regard, they are required for long periods, in repetitive processes that must comply with strict tolerance ranges. Hence, the positional accuracy of the robots is critical, since degradation of this can represent a considerable loss of resources. In recent years, prognosis and health management (PHM) methodologies, based on machine and deep learning, have been applied to robots, in order to diagnose and detect faults and identify the degradation of robot positional accuracy, using external measurement systems, such as lasers and cameras; however, their implementation is complex in industrial environments. In this respect, this paper proposes a method based on discrete wavelet transform, nonlinear indices, principal component analysis, and artificial neural networks, in order to detect a positional deviation in robot joints, by analyzing the currents of the actuators. The results show that the proposed methodology allows classification of the robot positional degradation with an accuracy of 100%, using its current signals. The early detection of robot positional degradation, allows the implementation of PHM strategies on time, and prevents losses in manufacturing processes. Full article

Knowing the importance of assuring their reliability and availability, prognosis and remaining useful life calculation (RUL) concepts are highly suggested to be applied in critical applications such as hybrid electric vehicles (HEV). In the electrical propulsion system of HEVs, the electrical machine is one of the most critical elements considering its cost and function. Most electrical machines used in HEVs are permanent magnet machines (PMM). Most severe faults in PMM that affect its normal operation are the result of demagnetization. However, applying prognosis to a real prototype to detect the presence of mechanical defects such as cracks in the magnet of PMM and calculating the RUL of this defective element are challenging. In this paper, we are going to take advantage of a finite element model already built for the PMM in the healthy state and the state where cracks of different depths are integrated into the magnet. After that, relevant vital parameters that are affected when this type of fault persists in the machine are collected. Then, prognosis is applied to detect the presence of the crack in one piece of magnet in the electrical machine. Following this, the RUL calculation is performed to predict the remaining time before the crack propagates and a total fracture occurs in the magnet. The method used to execute the prognosis is the hidden Markov model (HMM). The RUL calculation will be performed using Paris equation, being the most important equation that models the growth and propagation of cracks Full article

Internet of things (IoT) applications, which include environmental sensors and control of automated manufacturing systems (AMS), are growing at a rapid rate. In terms of hardware and software designs, communication protocols, and/or manufacturers, IoT devices can be extremely heterogeneous. Therefore, when these devices are interconnected to create a complicated system, it can be very difficult to detect and fix any failures. This paper proposes a new reliability design methodology using “colored resource-oriented Petri nets” (CROPNs) and IoT to identify significant reliability metrics in AMS, which can assist in accurate diagnosis, prognosis, and resulting automated repair to enhance the adaptability of IoT devices within complicated cyber-physical systems (CPSs). First, a CROPN is constructed to state “sufficient and necessary conditions” for the liveness of the CROPN under resource failures and deadlocks. Then, a “fault diagnosis and treatment” technique is presented, which combines the resulting network with IoT to guarantee the reliability of the CROPN. In addition, a GPenSIM tool is used to verify, validate, and analyze the reliability of the IoT-based CROPN. Comparing the results to those found in the literature shows that they are structurally simpler and more effective in solving the deadlock issue and modeling AMS reliability. Full article