Automated Condition Monitoring Research Articles

Application of Anomaly Detection to Real World Monitoring Data of a Rail-Road Bridge

The growing importance of automatic bridge condition monitoring, driven by the need to sustain infrastructure and reduce construction-related CO2 emissions, has led to increased deployment of sensor technologies on bridges. However, the overwhelming volume of data generated necessitates innovative approaches. Machine learning (ML), particularly the Regression Based Thermal Response Prediction Method (RBTRPM), offers a promising solution. RBTRPM employs ML models to predict structural responses from temperature measurements. These predictions are compared with actual responses to identify anomalies. While introduced in 2014, its practical applicability has been limited. This paper successfully translates RBTRPM into practice, exploring key considerations. A measurement duration of at least six months is recommended, though a longer period would provide even better results. It is also important to emphasize the preference for using only structural influences in the response predictions. The study introduces the Peak over Threshold method for threshold determination applied on RBTRPM and employs moving metrics for concise result presentation, providing valuable insights for the implementation of RBTRPM in real-world bridge monitoring scenarios.

Research on Pantograph Defect Classification Based on Vibration Signals.

Pantograph-based electrical current transmission systems are used in electric traction vehicles. The contact surface between the pantograph and the catenary wire experiences mechanical and thermal effects during the train's movement. Typically, this contact surface on the pantograph is covered by a segmented carbon or copper rod, attached to an aluminum base. Railways implement organizational measures for pantograph condition monitoring, based on scheduled inspections. Constitutionally, the option to replace contact elements or individual segments of the pantograph exists if wear is detected. Many scientific publications describe ideas for pantograph visualization and automated condition monitoring. These ideas are based on analyzing mechanical vibrations generated by the pantograph, acoustic vibration signal analysis, 3D geometric data of the pantograph surface captured by laser scanning, and combinations of several methods. However, in these publications, mechanical vibration analysis is limited to signal shape and spectral analysis. The possibility of treating the vibration signal as a random process using statistical methods has not been utilized. This study describes the possibility of evaluating classified mechanical pantograph vibrations using the signal's autocorrelation transformation. A laboratory experiment confirmed the proposed method for evaluating informative signal classification features. The proposed method can distinguish between signals generated by a defective pantograph surface and identify different types of defects.

Unsupervised fault detection using frequency-wise angular filtering in contaminated vibration signals

Manufacturing processes involve multiple machines within a production line. Unexpected faults in machines reduce productivity and increase maintenance costs. Engineers face difficulties in managing numerous machines individually and controlling them immediately. For automatic condition monitoring, several studies have focused on multivariate statistical process control and fault detection based on artificial intelligence. These methods require labeled data or assume that the training data contains only normal patterns. However, obtaining labeled data in the industry is challenging because engineers must manually label the data. Contaminated signals containing fault patterns in unlabeled training data significantly degrade the performance of fault detection in the model. This study proposes Unsupervised fault detection with Frequency-wise Angular Filtering (UFAF) to improve the performance of fault detection in contaminated vibration signals. The UFAF extracts angular features to estimate the normal samples for use only during model training. This filtering strategy is repeated at every epoch and is eventually optimised to use only high-quality normal samples during model training. An experiment using SpectraQuest gearbox datasets confirms the excellent performance for contaminated signals, as angular features are effective in identifying normal and fault signals. The UFAF is practical and applicable in industries wherein it is difficult to collect labeled data.

Modeling of Ball Screw–Nut Interface Stiffness With Wear (Ball Screw Wear Dynamics)

Abstract The effects of wear, preload loss, and missing balls on the dynamics of ball screw drives in machine tools are modeled and incorporated into the finite element model of the drive assembly for condition monitoring. The contacts between the ball–nut and ball–screw are modeled using Hertzian springs, whose stiffnesses vary as a function of the worn contact area. These contact stiffnesses are then transformed to the finite element nodes on the nut and screw. The frequency response functions at the motor shaft and table, which can be measured by commercial computer numerical control (CNC), are predicted for various faults at different positions of the table. The experimentally validated model demonstrates that the faults primarily affect the first coupled torsional-axial mode of the ball screw drive and can be utilized for automated condition monitoring of ball screw drives.

Впровадження автоматизованих систем моніторингу мостових споруд в Україні: Світовий досвід та національні перспективи

ntroduction. Highway bridges are an important element of the transport infrastructure of any country, and Ukraine is no exception. Their safe operation is critical to ensure the sustainable functioning of logistics systems and economic development of regions. However, due to constant loads, the impact of natural factors and insufficient funding for maintenance, a significant number of bridges in Ukraine are in poor technical condition. Accordingly, it is necessary to take measures to diagnose the condition of bridges in a timely manner and prevent possible accidents. In this context, automated condition monitoring systems (ACMS) are an effective tool for ensuring the smooth operation of such structures. Problem Statement. There are more than 16 thousand bridges on public roads of national importance in Ukraine, more than half of which do not meet modern standards for dimensions and load capacity. The problem is particularly acute for bridges that have been in operation for more than 40 years, as they are more prone to accidents. Due to the lack of systematic monitoring and control of the technical condition of such structures, there is a threat of their destruction. In the world practice, automated monitoring systems are actively used to prevent such situations. In Ukraine, the need to implement such systems is becoming increasingly relevant in the face of increased requirements for infrastructure security.

Comparative study between two artificial intelligence techniques (NN and SVM) applied in fault diagnosis of Wind Turbine

This paper addresses the application of artificial intelligence (AI) techniques for the classification of faults in wind turbine. Wind Energy Conversion Systems have become a focal point in the research of renewable energy sources. Reliability of wind turbine is critical to extract this maximum amount of energy from the wind. Many condition monitoring techniques that are based on steady-state analysis are being applied to wind generators. Bearing faults in this generator causes mechanical vibrations and the variations in the air gap density. The air gap flux density is modulated and currents are generated at different frequencies related to the mechanical vibrations. Characteristic bearing frequencies are associated with the physical construction of bearing and its failure mode. Classical spectral analysis using the Fourier transform was used to detect different bearing failure modes. The magnitudes of the frequencies components formed by the bearing defect are small compared to the rest of the current spectrum. This large difference in magnitude makes difficult the detection and classification of bearing faults. In this paper, we show that the utilization of different approaches of artificial intelligence such as neuronal network (NN) and support vector machines (SVM) can be discriminate different failure modes and gives a good basis for an automatic and non-invasive condition monitoring for wind turbine.

Towards next generation power grid transformer for renewables: Technology review

AbstractThis paper develops a technical framework for the next‐generation power grid transformer (NGPGT) for grid renewables. This framework is structured to overcome the environmental challenges produced by the explosive use of nonrenewable base energy generation sources. The use of these sources cannot meet the required electricity for the world's growing community due to their availability, cost, and lack of flexibility. However, modern energy systems focus on the use of renewable energy sources, where the grid transformer's interaction plays an essential role in their generation, transmission, and distribution. The lack of centralization, local monitoring, interoperability, authenticity, and precise bi‐directional flow may limit the application of current framework power grid transformers in grid renewables. In this paper, a new technical framework, called NGPGT, is developed by introducing some extended features for addressing the challenges shown in current‐generation transformers. This is structured by enabling some advanced technical features with the existing framework, which includes automatic condition monitoring, intelligent inverters, edge computing, automatic controlling, and intelligent management. This paper also illustrates the benefits and scope of the NGPGT compared to the existing transformer by assembling essential requirements and obligatory components. Additionally, this paper highlights a few difficulties of implementing NGPGT in terms of operational, communication, energy management, and economic points of view, which may enable further research scopes for the researchers.

An orbit-based encoder–forecaster deep learning method for condition monitoring of large turbomachines

Most data-driven methods used in condition monitoring and early faults alarming of large turbomachines need to manually extract features for modeling, which inevitably introduces subjectivity and uncertainties. In addition, conventional deep learning methods are able to automatically classify fault types using shaft orbit images constructed from the vibration data, but image-based deep learning methods have yet been investigated for the long-term condition monitoring of a turbomachine. This study presented a novel image-based encoder–forecaster deep learning model, OrbitLSTM, to address these issues by modeling the dynamic behaviors of orbit image sequences for condition monitoring of turbomachines. The model is constructed and trained by using orbit image series in the healthy operation status through convolutional long short-term memory network. Then the temporal evolution of orbit shapes is predicted by the trained OrbitLSTM model, and used as a reference to evaluate the machine status under unknown conditions. The early fault warning is triggered when the mean square error or the structural similarity index between measurements and predictions exceeds the predefined threshold. The proposed model seamlessly combines the CNN’s capability of automatic feature extraction from images and the LSTM’s advantage of long-term memory in historical sequence behaviors. The capability and accuracy of the proposed model for fault monitoring and early warning are demonstrated using both the simulation data and the raw vibration signals collected from sensors installed in different real-world industrial turbomachines. This study provides a new image-based deep learning method for automatic condition monitoring towards the predictive maintenance of large turbomachines in various industrial fields.

A New Approach to Study the Effect of Complexity on an External Gear Pump Model to Generate Data Source for AI-Based Condition Monitoring Application

The external gear pump, like any other hydraulic component, is vulnerable to failure, which may lead to downtime as well as the failure of other components linked to it, thereby causing production loss. Therefore, establishing a condition monitoring system is crucial in identifying failure at an early stage. Traditional condition monitoring approaches rely on experimental data that are collected by means of sensors. However, the sensors utilized in the experiments may have calibration issues, which lead to inaccurate measurements. The availability of experimental data is also limited as it is difficult and expensive to create and detect a fault in a component. Hence, it is essential to develop a simulation model that mimics the performance of the actual system. The data generated from the model can be utilized to create the data source required for automated condition monitoring. A new methodology based on a detailed geometric model for simulating the External Gear Pump is described and compared to two models analyzed in the authors’ previous work, namely Schlosser’s loss model and simple geometric model. In this paper, the three models are compared with experimental data and the method utilized for fault injection. Schlosser’s loss model, as well as the detailed geometric model, are found to be suitable in terms of validation; however, the latter is a better candidate in terms of fault injection. Hence, the detailed geometric model can be implemented as a tool to generate the data source for condition monitoring applications.

AI-Enabled Vibrotactile Feedback-Based Condition Monitoring Framework for Outdoor Mobile Robots

An automated Condition Monitoring (CM) and real-time controlling framework is essential for outdoor mobile robots to ensure the robot’s health and operational safety. This work presents a novel Artificial Intelligence (AI)-enabled CM and vibrotactile haptic-feedback-based real-time control framework suitable for deploying mobile robots in dynamic outdoor environments. It encompasses two sections: developing a 1D Convolutional Neural Network (1D CNN) model for predicting system degradation and terrain flaws threshold classes and a vibrotactile haptic feedback system design enabling a remote operator to control the robot as per predicted class feedback in real-time. As vibration is an indicator of failure, we identified and separated system- and terrain-induced vibration threshold levels suitable for CM of outdoor robots into nine classes, namely Safe, moderately safe system-generated, and moderately safe terrain-induced affected by left, right, and both wheels, as well as severe classes such as unsafe system-generated and unsafe terrain-induced affected by left, right, and both wheels. The vibration-indicated data for each class are modelled based on two sensor data: an Inertial Measurement Unit (IMU) sensor for the change in linear and angular motion and a current sensor for the change in current consumption at each wheel motor. A wearable novel vibrotactile haptic feedback device architecture is presented with left and right vibration modules configured with unique haptic feedback patterns corresponding to each abnormal vibration threshold class. The proposed haptic-feedback-based CM framework and real-time remote controlling are validated with three field case studies using an in-house-developed outdoor robot, resulting in a threshold class prediction accuracy of 91.1% and an effectiveness that, by minimising the traversal through undesired terrain features, is four times better than the usual practice.

AI-Enabled Condition Monitoring Framework for Indoor Mobile Cleaning Robots

Autonomous mobile cleaning robots are ubiquitous today and have a vast market need. Current studies are mainly focused on autonomous cleaning performances, and there exists a research gap on monitoring the robot’s health and safety. Vibration is a key indicator of system deterioration or external factors causing accelerated degradation or threats. Hence, this work proposes an artificial intelligence (AI)-enabled automated condition monitoring (CM) framework using two heterogeneous sensor datasets to predict the sources of anomalous vibration in mobile robots with high accuracy. This allows triggering proper maintenance or corrective actions based on the condition of the robot’s health or workspace, easing condition-based maintenance (CbM). Anomalous vibration sources are classified as induced by uneven Terrain, Collision with obstacles, loose Assembly, and unbalanced Structure, which causes accelerated system deterioration or potential hazards. Here, an unexplored heterogeneous sensor dataset using inertial measurement unit (IMU) and current sensors is proposed for effective recognition across different vibration classes, resulting in higher-accuracy prediction. A simple-structured 1D convolutional neural network (1D CNN) is developed for training and real-time prediction. A 2D CbM map is generated by fusing the predicted classes in real time on an occupancy grid map of the workspace to monitor the conditions of the robot and workspace remotely. The evaluation test results of the proposed method show that the usage of heterogeneous sensors performs significantly more accurately (98.4%) than previous studies, which used IMU (92.2%) and camera (93.8%) sensors individually. Also, this model is comparatively fast, fit for the environment, and ideal for real-time applications in mobile robots based on field trial validations, enhancing mobile robots’ productivity and operational safety.

AI-Enabled Condition Monitoring Framework for Outdoor Mobile Robots Using 3D LiDAR Sensor

An automated condition monitoring (CM) framework is essential for outdoor mobile robots to trigger prompt maintenance and corrective actions based on the level of system deterioration and outdoor uneven terrain feature states. Vibration indicates system failures and terrain abnormalities in mobile robots; hence, five vibration threshold classes for CM in outdoor mobile robots were identified, considering both vibration source system deterioration and uneven terrain. This study proposes a novel CM approach for outdoor mobile robots using a 3D LiDAR, employed here instead of its usual use as a navigation sensor, by developing an algorithm to extract the vibration-indicated data based on the point cloud, assuring low computational costs without losing vibration characteristics. The algorithm computes cuboids for two prominent clusters in every point cloud frame and sets motion points at the corners and centroid of the cuboid. The three-dimensional vector displacement of these points over consecutive point cloud frames, which corresponds to the vibration-affected clusters, are compiled as vibration indication data for each threshold class. A simply structured 1D Convolutional Neural Network (1D CNN)-based vibration threshold prediction model is proposed for fast, accurate, and real-time application. Finally, a threshold class mapping framework is developed which fuses the predicted threshold classes on the 3D occupancy map of the workspace, generating a 3D CbM map in real time, fostering a Condition-based Maintenance (CbM) strategy. The offline evaluation test results show an average accuracy of vibration threshold classes of 89.6% and consistent accuracy during real-time field case studies of 89%. The test outcomes validate that the proposed 3D-LiDAR-based CM framework is suitable for outdoor mobile robots, assuring the robot’s health and operational safety.

Reliable Ship Emergency Power Source: A Monte Carlo Simulation Approach to Optimize Remaining Capacity Measurement Frequency for Lead-Acid Battery Maintenance

<div>The development of predictive maintenance has become one of the most important drivers of innovation, not only in the maritime industry. The proliferation of on-board and remote sensing and diagnostic systems is creating many new opportunities to reduce maintenance costs and increase operational stability. By predicting impending system faults and failures, proactive maintenance can be initiated to prevent loss of seaworthiness or operability. The motivation of this study is to optimize predictive maintenance in the maritime industry by determining the minimum useful remaining lead-acid battery capacity measurement frequency required to achieve cost-efficiency and desired prognostic performance in a remaining battery capacity indication system. The research seeks to balance operational stability and cost-effectiveness, providing valuable insight into the practical considerations and potential benefits of predictive maintenance. The methodology employed in this study includes outlining the theoretical development of a fully automated condition monitoring system and describing data cleansing steps to account for environmental effects on system performance. A Monte Carlo simulation is used to evaluate the sensitivity of the remaining useful life prediction to varying measurement frequencies, prediction models, and parameter settings, leading to an estimate of the optimal measurement frequency for the system. The results show that a certain minimum measurement frequency is required to achieve the target prediction accuracy while balancing cost-efficiency and operational stability. Reliable failure prediction with negligible changes in prognostic accuracy can be achieved by performing useful remaining lead-acid battery capacity measurements twice a day or every 5 ship voyage cycles with the underlying utilization.</div>

Bridging the trust gap: Evaluating feature relevance in neural network-based gear wear mechanism analysis with explainable AI

Neural networks (NNs) have attracted a lot of attention in recent years in automated condition monitoring assessment. Scientists have successfully demonstrated that NNs can analyze mould and oil images simply on the basis of labeled data, without the need for expert knowledge, and provide an accurate indication of the wear mechanism and wear severity present. However, these results also raise questions about the generalizability and way of finding solutions of NNs in the field of severity and wear mechanism analysis. Since NNs are designed as a black-box, it is not obvious without further analysis which features of the input data are particularly relevant for the classification. Methods from the field of explainable Artificial Intelligence (XAI) can provide this analysis. If it is known which input areas of an image are particularly relevant for the NN, a targeted analysis can be used to make a statement about the meaningfulness of the solution finding and thus strengthen the trust in the NN. In this work, Layer-wise Relevance Propagation (LRP) is applied to an established convolution neural network (CNN) for tribological image analysis. Subsequently, an automated global meta-analysis of the LRP results on the relevant clusters of the mould and oil gearbox images is used to make a statement about the way the network is classified and compared with the approach of experts. This analysis proves that a CNN is able to base its classification on similar guidelines as a domain expert. Furthermore, it is shown that a misclassification of the network, which lowers the accuracy, is not due to a wrong assignment, but rather to the unclear manually performed labeling. The output of the method is an overview, which lists the most defining features for each severity class. Based on this, a domain expert can reliably and quickly determine whether the NN uses meaningful features to determine the solution.

Photovoltaic Module Fault Detection Based on Deep Learning Using Cloud Computing

The performance of photovoltaic modules (PVMs) degrades due to the occurrence of various faults such as discoloration, snail trail, burn marks, delamination, and glass breakage. This degradation in power output has created a concern to improve PVM performance. Automatic inspection and condition monitoring of PVM components can handle performance-related issues, especially for installed capacity where no trained personnel are available at the location. This paper describes a deep learning-based technique involving convolutional neural networks (CNNs) to extract features from aerial images obtained from unmanned aerial vehicles (UAVs) and classify various types of fault occurrences using cloud computing and Internet of things (IoT). The algorithm used demonstrates a binary classification with high accuracy by comparing individual faults with good condition. Efficient and effective fault detection can be observed from the results obtained.

A novel online tool condition monitoring method for milling titanium alloy with consideration of tool wear law

Due to issues such as limited variability in monitoring data across different tool wear conditions and interference during the machining process, data-driven monitoring models are susceptible to misclassification. Therefore, this paper proposes a pioneering approach that takes into account the tool wear law and the characteristic distribution of tool wear monitoring data. Specifically, the paper proposes an automatic feature extraction and tool condition monitoring method based on Siamese Long Short-term Memory Networks (SLSTMs) to transform the original data distribution, enhance the differentiation of different tool wear condition monitoring data, and achieve accurate prediction of tool wear condition. Additionally, a hybrid data and mechanism-driven tool wear monitoring method is proposed that takes advantage of the fact that tool wear is continuous and irreversible to enable reliable adjustment of the monitoring results of the data-driven model without human intervention. The study conducted milling experiments on a three-axis vertical machining center, using TA2 titanium alloy as the workpiece material. Vibration signals from the spindle in three directions were collected as input to the network, with tool wear state labeled as “0″ or ”1″ as the output. Classical machine learning algorithms such as Decision Trees, K-Nearest Neighbors (KNN) and Support Vector Machines (SVM), as well as classical deep learning algorithms such as Convolutional Neural Networks (CNN), Sparse Stacked Autoencoders (SSAE) and Long Short-Term Memory Neural Networks (LSTM), were used to construct and compare feature extraction and state monitoring models. The proposed model based on Siamese Long Short-Term Memory Neural Networks (SLSTMs) achieved testing average accuracy of 98.2% (σ2 = 0.44), outperforming typical deep learning algorithms such as CNN, LSTM and SSAE as well as traditional machine learning algorithms such as Decision Trees, KNN and SVM in terms of accuracy and robustness. Moreover, the proposed data and mechanism hybrid-driven tool wear monitoring method can effectively improve the monitoring accuracy and robustness of data-driven models such as CNN and SSAE. The monitoring accuracy of CNN and SSAE increased from 95.9% to 97.6% and 91.0% to 94.3%, respectively.

Self-powered vibration sensor based on the coupling of dual-mode triboelectric nanogenerator and non-contact electromagnetic generator

Random vibration of mechanical equipment will lead to structural fatigue damage, while strong and sustained vibration will lead to the overall performance decline of the equipment. Vibration sensors can be used to monitor the stability of mechanical operation. However, in some complex unattended environments, it is still a significant challenge to realize real-time equipment monitoring and early warning. Therefore, we propose a self-powered multifunctional vibration sensor based on coupling a dual-mode triboelectric nanogenerator and a non-contact electromagnetic generator (M-HNG) for automatic condition monitoring. The dual-mode triboelectric nanogenerator and non-contact electromagnetic generator can work together to realize real-time self-powered vibration monitoring. We established the prototype model of the vibration sensor, simulated the working principle of each part of the sensor, and fitted the functional relationship between the vibration amplitude and speed of the sensor and the output electrical signal. The results show that the short-circuit current of the sensor is very sensitive to the change in external vibration speed and has good stability in its detection range. Using a non-contact electromagnetic generator can effectively improve the service life of the vibration sensor. The system can continuously monitor the vibration signals generated by machinery during operation and provide an effective way for structural health monitoring, especially in extreme environments or unattended conditions.

An optical flow-based method for condition-based maintenance and operational safety in autonomous cleaning robots

Autonomous professional cleaning robots are ubiquitous today, coexist with humans, and the demand continues to improve hygiene and productivity, especially in large indoor workplaces. Hence, a proper maintenance strategy is essential to monitor the robot’s health, assuring flawless operation and safety. Manual supervision and periodic maintenance are adopted in general, which is challenging to detect failures in advance, resulting in higher maintenance costs and operational hazards. Anomalous vibration due to system degradation or environmental factors is an early symptom of failure or potential threats. Hence, predicting the sources of such abnormal vibration will help prompt maintenance action or set a hazard-free environment. However, such condition-monitoring research studies are not common for indoor cleaning robots. To fill this gap, we proposed an automated Condition Monitoring (CM) system, predicting the abnormal vibration sources that will enhance Condition-based Maintenance (CbM) and Operational Safety. A novel vibration-based CM method is presented for mobile robots, suitable for indoor environments under typical illumination states, using a monocular camera and optical flow technique instead of their usual perception-related applications. The sources of abnormal vibration were classified as terrain, collision, loose assembly, and structural imbalance. We modelled vibration as sparse optical flow, and the change in optical flow vector displacement due to the robot’s vibration is derived from consecutive camera frames used as vibration data. A practical framework is developed adopting the One Dimensional Convolutional Neural Network (1D CNN) model to fit this vibration data and tested predicting the vibration source class with an average accuracy of 93.8%. In addition, a vibration source map (CbM map) is proposed by fusing the predicted class in the workplace environment map for real-time monitoring. The case studies conducted using our in-house-developed cleaning robot show that the proposed CM framework will help the maintenance team for CbM, operational safety, and select proper maintenance strategies.

Fleet-based early fault detection of wind turbine gearboxes using physics-informed deep learning based on cyclic spectral coherence

The development of a reliable and automated condition monitoring methodology for the detection of mechanical failures in rotating machinery has garnered much interest in recent years. Thanks to the rise in popularity of machine learning techniques, the number of purely data-driven approaches that try to tackle the issue of vibration-based condition monitoring has also drastically improved. Instead of directly using the vibration measurement data as input to a machine learning model, this work first exploits the cyclostationary characteristics inherent to vibration waveforms originating from rotating machinery. The proposed methodology first estimates the two-dimensional cyclic spectral coherence map of a vibration signal in order to decompose the cyclic modulations on the cyclic and carrier frequency plane. While this provides an effective tool to visualize any potential modulation signatures of faulty gears or bearings, it does not allow for easy inspection over time due to its dimensions. To tackle this issue, this paper proposes an unsupervised deep learning approach to wield this vast amount of data as a tool for detecting persistent changes in the modulation characteristics of the vibration signals. In the first phase, a deep autoencoder learns to reconstruct predictions of the cyclic coherence maps based on unlabeled healthy vibration data and the machine operating conditions. Two post-processing steps improve the predictions by mitigating frequency shifts and outlier or noisy measurements. Lastly, the residual error between the predicted and the actual coherence map is then aggregated and employed for further alarming based on thresholds. The autoencoder model is trained using five years of gearbox vibration data from five different wind turbines. The methodology is then validated on two faulty and eight healthy turbines. The results confirm that the proposed approach can deliver clear indications of failure for the faulty turbine while being completely devoid of any significant alarm trends for the healthy turbines. Thanks to the combination of highly effective cyclostationary signal processing with deep learning while using the operating conditions, the proposed methodology can detect and track incipient mechanical faults from non-stationary vibration data of rotating machinery. Lastly, it is important to emphasize that the proposed method is capable of learning the healthy behavior on one turbine and predicting the expected behavior on another turbine.

Numerical and random forest modelling of the impact response of hierarchical auxetic structures

Major sources of concern when auxetic protective structures are deployed in service of mission-critical applications encompass the triggering of high impact stress and the weakening of the structures’ elastic strength in response to the impact events. The current prevailing approach to assessing the impact resistance of these structures broadly hinges on mechanics-informed nonlinear finite element (FE) analysis. However, this method is computationally expensive and ill-suited for tackling the implementation of automated condition monitoring schemes. To address these issues, first, this paper proposes a hybrid hierarchical auxetic structure named Hybrid-Hierarchical Re-entrant Honeycomb (HHRH) that is endowed with impact stress-reducing capabilities. Next, using explicit FE, the investigation uncovers the interplay between the key geometric features of this HHRH auxetic structure and the impact performance under low, intermediate, and high crushing velocities. The outcome of the nonlinear explicit FE simulations is then unified with random forests (RF) scheme towards the establishment of intelligent auxetic structural systems. The results revealed that the developed HHRH maintained the auxeticity of the regular re-entrant auxetic and exhibited superior performance in some crushing strain regions. Moreover, the HHRH structure manifests up to an 85 % reduction in peak stress and the proposed reinforcement boosts the auxetic property by up to 23 % when compared to the regular re-entrant auxetic structure under high-velocity applications. Regarding the established data-driven RF-enabled machine learning model, its predictive strength with optimally-tuned hyperparameters is demonstrated to excellently capture the nonlinear multi-modal crushing stress response at various crushing strains, velocities, and geometric variations. Data AvailabilityThe raw/processed data required to reproduce these findings cannot be shared at this time as the data also forms part of an ongoing study.