Search Results (162)

The inter-shaft bearing is an important component of aero-engine rotor systems. It works between a high-pressure rotor and a low-pressure rotor. Effective fault diagnosis of it is significant for an aero-engine. The casing vibration signals can promptly and intuitively reflect changes in the operational health status of an aero-engine’s support system. However, affected by a complex vibration transmission path and vibration of the dual-rotor, the intrinsic vibration information of the inter-shaft bearing is faced with strong noise and a dual-frequency excitation problem. This excitation is caused by the wide span of vibration source frequency distribution that results from the quite different rotational speeds of the high-pressure rotor and low-pressure rotor. Consequently, most existing fault diagnosis methods cannot effectively extract inter-shaft bearing characteristic frequency information from the casing signal. To solve this problem, this paper proposed the denoised improved envelope spectrum (DIES) method. First, an improved envelope spectrum generated by a spectrum subtraction method is proposed. This method is applied to solve the multi-source interference with wide-band distribution problem under dual-frequency excitation. Then, an improved adaptive-thresholding approach is subsequently applied to the resultant subtracted spectrum, so as to eliminate the influence of random noise in the spectrum. An experiment on a public run-to-failure bearing dataset validates that the proposed method can effectively extract an incipient bearing fault characteristic frequency (FCF) from strong background noise. Furthermore, the experiment on the inter-shaft bearing of an aero-engine test platform validates the effectiveness and superiority of the proposed DIES method. The experimental results demonstrate that this proposed method can clearly extract fault-related information from dual-frequency excitation interference. Even amid strong background noise, it precisely reveals the inter-shaft bearing’s fault-related spectral components. Full article

The development of efficient and sustainable water recycling systems is essential for long-term human missions and the establishment of space habitats on the Moon, Mars, and beyond. Humidity condensate (HC) is a low-strength wastewater that is currently recycled on the International Space Station (ISS). The main contaminants in HC are primarily low-molecular-weight organics and ammonia. This has caused operational issues due to microbial growth in the Water Process Assembly (WPA) storage tank as well as failure of downstream systems. In addition, treatment of this wastewater primarily uses adsorptive and exchange media, which must be continually resupplied and represent a significant life-cycle cost. This study demonstrates the integration of a membrane-aerated biological reactor (MABR) for pretreatment and storage of HC, followed by brackish water reverse osmosis (BWRO). Two system configurations were tested: (1) periodic MABR fluid was sent to batch RO operating at 90% water recovery with the RO concentrate sent to a separate waste tank; and (2) periodic MABR fluid was sent to batch RO operating at 90% recovery with the RO concentrate returned to the MABR (accumulating salinity in the MABR). With an external recycle tank (configuration 2), the system produced 2160 L (i.e., 1080 crew-days) of near potable water (dissolved organic carbon (DOC) < 10 mg/L, total nitrogen (TN) < 12 mg/L, total dissolved solids (TDS) < 30 mg/L) with a single membrane (weight of 260 g). When the MABR was used as the RO recycle tank (configuration 1), 1100 L of permeate could be produced on a single membrane; RO permeate quality was slightly better but generally similar to the first configuration even though no brine was wasted during the run. The results suggest that this hybrid system has the potential to significantly enhance the self-sufficiency of space habitats, supporting sustainable extraterrestrial human habitation, as well as reducing current operational problems on the ISS. These systems may also apply to extreme locations such as remote/isolated terrestrial locations, especially in arid and semi-arid regions. Full article

Free stroke, which means the intermittent no-load operation state of dampers, can cause an abnormal noise and unavoidably lead to the deterioration of vehicle NVH performance. In electric vehicles, the noise is particularly intolerable because there are no engine sounds to mask it. Focusing on this, the mechanism of the free-stroke phenomenon is analyzed. A method, which involves parametric models and numerical simulation, is proposed to prevent free-stroke phenomena during the damper design phase. This paper proposes a free-stroke mechanism based on a fluid–structure interaction (FSI) numerical method, combined with experiments, which intends to provide a design reference with guaranteed performance for dampers. Initially, according to parametric cavitation models and by applying numerical methods, simulations for the proposed FSI model are calculated. By analyzing the simulation results, strain variation characteristics near the bottom of the damper valves are revealed, which establish the relationships between strain change, cavitation and the free-stroke phenomena. Meanwhile, the specific position and distribution of free-stroke failure are clearly located by running diverse loading speeds. Finally, all the theoretical analysis results are verified using damper noise tests and indicator bench tests. Full article

Cyclic triaxial tests are frequently used in the laboratory to assess the liquefaction susceptibility of soils. This paper will serve a two-fold purpose: First, it will serve to explain how the mechanics of the tests represent the stresses that occur in the field. Topics covered include the differences in the stress paths for the soil in the field and in the lab, the differences in the actual stresses applied in the lab and the field, the differences between stress-controlled and strain-controlled tests, and the effects of other aspects of the testing methodology. The development of adjustment factors for converting the laboratory test results to the field is also briefly discussed. The second purpose of the paper is to serve as a guide to interpreting cyclic triaxial test results. The topics covered will include an examination of the two main liquefaction modes and the impact that the failure criteria selected have on the analysis, the differences between stress-controlled and strain-controlled test results, energy dissipation, and pore pressure generation. The author has run more than 1500 cyclic triaxial tests over the course of his career. He has found that, while the test is fairly straightforward to perform, it requires a much deeper understanding of the test mechanics and data interpretation in order to maximize the information gained from performing the test. This paper is intended as a guide, helping engineers to gain further insights into the test and its results. It has a target audience encompassing both those who are running their first tests and those who are looking to increase their understanding of the tests they have performed. Full article

To enable proactive prediction of marine diesel engine failure time and root causes, thereby reserving sufficient time for maintenance, this study proposes a data-driven multi-algorithm integration framework for performance assessment and fault early warning in marine diesel engines. By integrating the SSD (steady-state detection) algorithm, a data-driven CLIQUE clustering algorithm was chosen for automatic multi-parameter high-dimensional running condition partitioning. This innovative approach overcomes the limitations of traditional single-parameter approaches or dimensionality reduction techniques, significantly enhancing state classification accuracy. The improved classification results subsequently increase the reliability of Mahalanobis distance as a performance indicator for marine diesel engine condition assessment. Finally, the cumulative anomaly method combined with the Yamamoto test was employed for anomaly detection analysis, enabling precise identification of fault occurrence time and establishing an effective early-warning mechanism. The study demonstrates that this technique effectively characterizes the overall performance of marine diesel engines and captures their performance degradation features. Implemented on a 6RT-flex82T marine diesel engine dataset, the method achieved precise prediction of fault occurrence time with early warnings, providing approximately 20 days advance notice for maintenance planning. Furthermore, comparative analyses with existing studies revealed its superior capability in pinpointing the anomaly to the jacket cooling water outlet temperature of cylinder #2. These results confirm the method’s effectiveness in both performance assessment and fault early warning for marine diesel engines, offering a novel approach for intelligent maintenance of shipboard equipment. Full article

This article analyses the potential use of a Line-Start Permanent-Magnet Synchronous Motor (LSPMSM) in a drive system with a frequency converter that enables stable operation without internal feedback from the rotor position. In Fault-Tolerant Control (FTC) drives, resistant to measuring sensor faults, classical PMSM machines lose the possibility of stable operation in the event of damage to the position/speed sensor. LSPMSMs can operate without the presence of measuring sensors. However, most existing studies focus on the application of LSPMSMs powered directly from the grid, which is a suitable approach for large machines such as pumps and fans. Given the ongoing efforts to improve the efficiency of electric drives, it is reasonable to explore the application of LSPMSMs in drives controlled by frequency converters. The key advantage of this approach is that the motor, which typically operates in a vector control structure, can maintain stable operation even in the event of a speed sensor failure. This article presents a comprehensive research approach. Calculations of a new type of induced-pole LSPMSM were carried out, and simulation tests using Ansys software were performed. Next, a prototype of the machine was made. The induced-pole PMSM contains a two-times-lower number of permanent magnets but their volume in the motor rotor is the same due to demagnetization robustness. The motor has enclosure-less construction. The startup and running characteristics of the motor were investigated under direct-on-line supply. The article presents calculations, simulation analyses, and experimental validation under scalar control, confirming the feasibility of using this type of machine in Fault-Tolerant Control drives. Full article

Bedding rock landslides, characterized by their distinct geological structure, are widely distributed and highly susceptible to sliding under external disturbances, resulting in catastrophic events. This study aims to unravel the geomechanical mechanisms governing rainfall-induced instability through an integrated investigation of a representative landslide in Xing’an Village, Chongqing. Employing multidisciplinary approaches, including field monitoring, geotechnical testing, and dynamic numerical modeling, we systematically revealed two critical failure zones: a front failure zone and a rear potential instability zone. Under rainstorm conditions, the safety factor for both zones was 1.02, indicating a marginally unstable state. The DAN-W simulations indicate that the potential instability zone at the rear of the landslide experienced complete failure within 12 s under heavy rainfall, with a maximum run-out distance of 20 m, a maximum velocity of 4.32 m/s, and a maximum deposition thickness of 8.3 m, which could potentially bury the buildings at the toe of the landslide. The low strength and permeability of the mudstone-dominated Badong Formation, characterized by interbedded mudstone, siltstone, and sandstone within the Middle Triassic geological system, provides a fundamental prerequisite for the landslide. Rainwater infiltration into the mudstone layers degraded its mechanical properties, and excavation at the slope base ultimately triggered the landslide initiation. These findings can provide theoretical support for preventing and managing similar bedding rock landslides with similar geological backgrounds. Full article

This work introduces an end-to-end methodology, from data gathering to fault notification, for the predictive maintenance of rotary components of machine tools. This is done through fingerprint routines; that is, processes that are executed periodically under the same no-load conditions to obtain a snapshot of the machine condition. High-frequency vibration data gathered during these routines combined with knowledge about the machine structure and its components are used to obtain failure-specific features. These features are then introduced to an anomaly and paradigm shifts detection algorithm. The method is evaluated through three distinct scenarios. First, we use synthetically generated data to test its ability to detect controlled variations and edge cases. Second, we use with publicly available data obtained from bearing run-to-failure tests under normal load conditions on a specially designed test rig. Finally, the methodology is validated using real-world data collected from a spindle bearing installed in a machine tool. The novelty of this work lies in performing anomaly detection using failure-specific features derived from fingerprint routines, ensuring stability over time and enabling precise identification of machine conditions with minimal data requirements. Full article

This study deals with the long-running challenge of joining similar and dissimilar materials using composite-to-composite and composite-to-metal joints. This research was conducted to evaluate the effects of surface morphology and surface treatments on the mechanical performance of adhesively bonded joints used for the aircraft industry. A two-segment, commercially available, toughened epoxy was chosen as the adhesive. Unidirectional carbon fiber prepreg and aluminum 2021-T3 alloys were chosen for the composite and metal panels, respectively. Surface treatment of the metal included corrosion elimination followed by a passive surface coating of Alodine^®. A combination of surface treatment methods was used for the composite and metal specimens, including detergent cleaning, plasma exposure, and sandblasting. The shear strength of the single-lap adhesive joint was evaluated according to the ASTM D1002. Ultraviolet (UV) and plasma exposure effects were studied by measuring the water contact angles. The test results showed that the aluminum adherent treated with sandblasting, detergent, and UV irradiation resulted in the strongest adhesive bonding of the composite-to-composite panels, while the composite-to-metal sample cleaned only with detergent resulted in the least bonding strength. The failure strain of the composite-to-composite bonding was reduced by approximately 50% with only sandblasting. However, extended treatment did not introduce additional brittleness in the adhesive joint. The bonding strength of the composite-to-composite panel improved by approximately 35% with plasma treatment alone because of the better surface functionalization and bonding strength. In the composite-to-aluminum bonding process, exposing the aluminum surface to UV resulted in 30% more joint strength compared to the Alodine^® coating, which suggests the origination of higher orders of magnitude of covalent groups from the surface. A comparison with published results found that the joint strengths in both similar and dissimilar specimens are higher than most other results. Detailed observations and surface analysis studies showed that the composite-to-composite bonding mainly failed due to adhesive and cohesive failures; however, failure of the composite-to-aluminum bonding was heterogeneous, where adhesive failure occurred on the aluminum side and substrate failure occurred on the composite side. Full article

Wave erosion of slopes can easily trigger landslides into marine environments and pose severe threats to both the ecological environment and human activities. Therefore, near-shore slope monitoring becomes crucial for preventing and alerting people to these potential disasters. To achieve a comprehensive understanding, it is imperative to conduct a detailed investigation into the dynamics of wave erosion processes acting on slopes. This research is conducted through flume tests, using a wave maker to create waves of various heights and frequencies to erode the slope models. During the tests, seismic signals, acoustic signals, and pore pressure generated by wave erosion and slope failure are recorded. Seismic and acoustic signals are analyzed, and time-frequency spectra are calculated using the Hilbert–Huang Transform to identify the erosion events and signal frequency ranges. Arias Intensity is used to assess seismic energy and explore the relationship between the amount of erosion and energy. The results show that wave height has a more decisive influence on erosion behavior and retreat than wave frequency. Rapid drawdown may potentially cause the slope to slide during cyclic swash and backwash wave action. As wave erosion changes from swash to impact, there is a significant increase in the spectral magnitude and Power Spectral Density (PSD) of both seismic and acoustic signals. An increase in pore pressure is observed due to the rise in the run-up height of waves. The amplitude of pore pressure will increase as the slope undergoes further erosion. Understanding the results of this study can aid in predicting erosion and in planning effective management strategies for slopes subject to wave action. Full article

Extracorporeal Carbon Dioxide Removal (ECCO₂R) systems support patients with severe respiratory failure. Concurrent ambulation and physical therapy improve patient outcomes, but these procedures are limited by the complexity and size of the extracorporeal systems and rapid changes in patient metabolism and the acid–base balance. Here, we present the first prototype of a wearable ECCO₂R system capable of adjusting to a patient’s changing metabolic needs. Exhaust gas CO₂ (EGCO₂) partial pressure is used as an analog for blood CO₂ partial pressure (pCO₂). Twin blowers modulate sweep gas through the AL to achieve a desired target EGCO₂. The integrated system was tested in vitro for 24 h with water, under varying simulated metabolic conditions and target EGCO₂ values, and in a single test with whole blood. When challenged with changing inlet water pCO₂ levels in in vitro tests, the system adjusted the sweep gas to achieve target EGCO₂ within 1 min. Control runs with a fixed sweep gas (without negative feedback) demonstrated higher EGCO₂ levels when challenged with higher water flow rates. A single in vitro test with whole ovine blood confirmed functionality in blood. This is the first step toward wearable ECCO₂R systems that automatically respond to changing metabolism. Such devices would facilitate physical therapy and grant greater autonomy to patients. Full article

Optimizing energy consumption is an important aspect of industrial competitiveness, as it directly impacts operational efficiency, cost reduction, and sustainability goals. In this context, anomaly detection (AD) becomes a valuable methodology, as it supports maintenance activities in the manufacturing sector, allowing for early intervention to prevent energy waste and maintain optimal performance. Here, an AD-based method is proposed and studied to support energy-saving predictive maintenance of production lines using time series acquired directly from the field. This paper proposes a deep echo state network (DeepESN)-based method for anomaly detection by analyzing energy consumption data sets from production lines. Compared with traditional prediction methods, such as recurrent neural networks with long short-term memory (LSTM), although both models show similar time series trends, the DeepESN-based method studied here appears to have some advantages, such as timelier error detection and higher prediction accuracy. In addition, the DeepESN-based method has been shown to be more accurate in predicting the occurrence of failure. The proposed solution has been extensively tested in a real-world pilot case consisting of an automated metal filter production line equipped with industrial smart meters to acquire energy data during production phases; the time series, composed of 88 variables associated with energy parameters, was then processed using the techniques introduced earlier. The results show that our method enables earlier error detection and achieves higher prediction accuracy when running on an edge device. Full article

The development of artificial intelligence-based tools is having a big impact on industry. In this context, the maintenance operations of important assets and industrial resources are changing, both from a theoretical and a practical perspective. Namely, conventional maintenance reacts to faults and breakdowns as they occur or schedules the necessary inspections of systems and their parts at fixed times by using statistics on component failures, but this can be improved by a predictive maintenance based on the real component’s health status, which is inspected by appropriate sensors. In this way, maintenance time and costs are saved. Improvements can be achieved even in the marine industry, in which complex ship propulsion systems are produced for operation in many different scenarios. In more detail, data-driven models, through machine learning (ML) algorithms, generate the expected values of monitored variables for comparison with real measurements on the asset, for a diagnosis based on the difference between expectations and observations. The first step towards realization of predictive maintenance is choosing the ML algorithm. This selection is often not the consequence of an in-depth analysis of the different algorithms available in the literature. For that reason, here the authors propose a framework to support an initial implementation stage of predictive maintenance based on a benchmarking of the most suitable ML algorithms. The comparison is tested to predict failures of the oil circuit in a diesel marine engine as a case study. The algorithms are compared by considering not only the mean squared error between the algorithm predictions and the data, but also the response time, which is a crucial variable for maintenance. The results clearly indicate the framework well supports predictive maintenance and the prediction error and running time are appropriate variables to choose the most suitable ML algorithm for prediction. Moreover, the proposed framework can be used to test different algorithms, on the basis of more performance indexes, and to apply predictive maintenance to other engine components. Full article

The inherent brittle behavior of cementitious composite is considered one of its weaknesses in structural applications. This study evaluated the impact strength and failure modes of composite U-shaped normal concrete (NC) specimens strengthened with polyurethane grout material (NC-PUG) subjected to repeated drop-weight impact loads (USDWIT). The experimental dataset was used to train and test three machine learning (ML) algorithms, namely decision tree (DT), Naïve Ba yes (NB), and K-nearest neighbors (KNN), to predict the three failure modes exhibited by U-shaped specimens during testing. The uncertainty of the failure modes under different uncertainty degrees was analyzed using Monte Carlo simulation (MCS). The results indicate that the retrofitting effect of polyurethane grout significantly improved the impact strength of concrete. During testing, U-shaped specimens demonstrated three major failure patterns, which included mid-section crack (MC), crushing foot (CF), and bend section crack (BC). The prediction models predicted the three types of failure modes with an accuracy greater than 95%. Moreover, the KNN model predicted the failure modes with 3.1% higher accuracy than the DT and NB models, and the accuracy, precision, and recall of the KNN model have converged within 300 runs of Monte Carlo simulation under different uncertainties. Full article

As an important part of industrial equipment, the safe and stable operation of rolling bearings is an important guarantee for the performance of mechanical equipment. Aiming at the problem that it is difficult to characterize the running state of rolling bearings, this paper mainly analyzes and processes the vibration signals of rolling bearings, extracts and fuses multi-information entropy, and monitors the running state of rolling bearings and predicts the remaining useful life prediction (RUL) through test verification. Firstly, in view of the difficulty in characterizing the bearings running state characteristics, a rolling bearings running state monitoring method based on multi-information entropy fusion and denoising autoencoder (DAE) was proposed to extract the multi-entropy index features of vibration signals to improve the accuracy of feature extraction, and to solve the problem of not obvious information representation of a single feature indicator and missing information in the feature screening process. Secondly, in view of the problems of low prediction accuracy and poor robustness and generalization in traditional RUL models, a rolling bearings RUL model combining convolutional autoencoder (CAE) and bidirectional long short-term memory network (BiLSTM) was proposed. The introduction of convolution operation made CAE have the feature of weight sharing, reducing the complexity of the model. Finally, the XJTU-SY data set was used to verify the constructed model. The results show that the condition monitoring model established in this paper can accurately evaluate the running state of the rolling bearing and accurately locate the failure time. At the same time, the residual life prediction model can realize the residual life prediction of most data sets, and has good accuracy and robustness. Full article