Search Results (1,754)

This study focuses on the reliability assessment of a series system composed of Weibull-distributed components. Because high-reliability components rarely fail during life testing or actual operation, conventional system reliability analysis methods based on failure time data do not work well. This paper presents a practical approach to address this issue, with a major interest in inferring the lower confidence limits of system reliability and reliable life. The proposed system reliability assessment method utilizes the minimum lifetime distribution theory to derive the closed-form confidence limits for system reliability indexes from Weibull zero-failure data. Furthermore, a system reliability update procedure is introduced, integrating life data at both the component and system levels. Monte Carlo simulations demonstrate that the proposed approach is more accurate than conventional methods. Finally, an engineering example of reliability assessment and life prediction for a satellite infrared Earth sensor is presented to illustrate the advantages and applications of the proposed method. Full article

Miniaturized components for enhanced integrated functionality or thin sheets for lightweight applications often consist of face-centered cubic metals. They exhibit good strength, corrosion resistance, formability and recyclability. Microfabrication technologies, however, may introduce cold work or detrimental heat-induced lattice defects into the material, with consequences for the mechanical properties. Austenitic stainless steels (1.4310, 1.4301) and aluminum alloys (EN AW-5005-H24, EN AW-6082-T6) were selected for this study. The influence of pulsed fiber laser cutting, microwaterjet cutting, and annealing on the strain–stress behavior was investigated. The micro-tensile test setup comprised a flex-structure force sensor, a laser extensometer, and a dedicated sample holder. Fiber laser cut 1.4310 samples exhibited early failure at low fracture strain in narrow shear band zones. The shear band zones were detectable on the sample surface, in the laser extensometer images, in the horizontal sections of the stress–strain curves, and in the microstructure. Inside the shear band zones, grains were strongly elongated and exhibited numerous parallel planar defects. Heat-induced chromium carbides, in combination with low stacking fault energy (SFE) and elevated carbon content, favored shear band zone formation in 1.4310. In contrast, microwaterjet cut high SFE materials EN AW-5005-H24 and EN AW-6082-T6, as well as low-carbon austenitic stainless steel 1.4301, exhibited uniform plastic deformation. Full article

Preserving ovarian function is important to women’s reproductive health. It is necessary for fertility and maintaining the overall hormonal balance. Platelet-rich plasma (PRP) is an autologous plasma containing a predominately platelet concentrate prepared from fresh blood. It has been observed that PRP injections into the ovary can renew the functional cells of the cortical layer of the ovary follicles and reactivate the production of sex hormones. It may improve a woman’s fertility in the case of premature ovarian failure, the condition after chemotherapy treatment, or during the climacteric period. The main markers to evaluate the procedure’s success are elevated anti-Müllerin hormone and enlarged count level of atrial follicles in ovaries. The aim of this review is to identify the ovarian PRP procedure success markers and point out the electrochemical sensor techniques. Literature was selected depending on including and excluding criteria; studies were sorted by topics in two blocks: PRP biomarkers and electrochemistry. As PRP acts as a regenerative care, electrochemical biosensors can provide accurate, real-time data to evaluate the biological response to PRP therapy. The biosensors’ ability to monitor hormonal levels and follicle development serves as objective markers of the effectiveness of PRP in restoring ovarian function. Together, these approaches enable a more precise evaluation of ovarian health and fertility outcomes after PRP intervention. Full article

Steam traps are essential for industrial systems, ensuring steam quality and energy efficiency by removing condensate and preventing steam leakage. However, their failure results in energy loss, operational disruptions, and increased greenhouse gas emissions. This paper proposes a novel predictive maintenance system for steam traps that integrates statistical time series features and transformer encoder–decoder models for fault diagnosis and visualization. The proposed system combines IoT sensor data, operational parameters, open data (e.g., weather information and public holiday calendars), machine learning, and two-dimensional diagnostic projection to improve reliability and interpretability. Experiments were conducted in two industrial plants: an aluminum processing plant and a food manufacturing plant, and the system achieved superior defect detection accuracy and diagnostic reliability compared to existing methods. The transformer-based model outperformed traditional methods, including random forest, gradient boosting, and variational autoencoder, in classification and clustering. The system also demonstrated an average 6.92% reduction in thermal energy across both sites, highlighting its potential to improve energy efficiency and reduce carbon emissions. This research highlights the transformative impact of AI-based predictive maintenance technologies in industrial operations and provides a framework for sustainable manufacturing practices. Full article

A fault-tolerant collaborative control strategy for four-wheel-drive electric vehicles is proposed to address hidden safety issues caused by one or more in-wheel motor faults; the basic design scheme is that the control system is divided into two layers of motion tracking and torque distribution, and three systems, including driving, braking, and front-wheel steering are controlled collaboratively for four-wheel torque distribution. In the layer of motion tracking, a vehicle model with two-degree-of-freedom is employed to predict the control reference values of the longitudinal force and additional yaw moment required; four types of sensors, such as wheel speed, acceleration, gyroscope, and steering wheel angle, are used to calculate the actual values. At the torque distribution layer, SSOD and MSCD distribution schemes are designed to cope with two operating conditions, namely sufficient and insufficient output capacity after local hub motor failure, respectively, focusing on the objective function, constraints, and control variables of the MSCD control strategy. Finally, two operating environments, a straight-line track, and a DLC track, are set up to verify the effectiveness of the proposed control method. The results indicate that, compared with traditional methods, the average errors of the center of mass sideslip angle and yaw rate are reduced by at least 12.9% and 5.88%, respectively, in the straight-line track environment. In the DLC track environment, the average errors of the center of mass sideslip angle and yaw rate are reduced by at least 6% and 4.5%, respectively. The proposed fault-tolerant controller ensures that the four-wheel-drive electric vehicle meets the requirements of handling stability and safety under one or more hub motor failure conditions. Full article

Currently, Industry 4.0 creates new opportunities for analyzing data on production processes and extracting knowledge from them. With the Internet of Things, data is continuously collected from machine sensors to analyze machine health. Thanks to artificial intelligence methods and discrete simulation, it is possible to process data and dynamically adjust the operating conditions of the production line to the expected time of failure-free operation of the machine or reliable work of an employee. Recently, machine learning techniques have been used to automatically adapt the production line to changes in a given production environment. The paper presents various methods of modeling actions, i.e., forecasting the failure-free operation time of a machine or the error-free working time of an employee. The possible actions the agent can perform, the possible prediction techniques that can be selected are presented. The time between failures is described by a log-normal distribution. The asymmetric lognormal distribution is much more flexible for practical modeling compared to the “perfectly” symmetric normal distribution. In practice, the asymmetric lognormal distribution, strongly shifted to the left, can be used to describe the decreasing time between failures due to human error, as well as the time between failures of a machine in the third phase of its life cycle, which decreases as the machine ages and its components wear out. The parameters of the distribution are estimated using the maximum-likelihood approach, theempirical moments approach, the renewal-theory approach, the empirical distribution function and the method based on coefficient of variation. Numerical examples of predicting failure-free operation times described by the log-normal distribution are presented. The results are compared assuming that failure-free times are described by exponential, normal and Weibull distributions. The results are also compared with an example of the simplest learning method. Full article

The working environment at coal mining faces is harsh, leading to high failure rates and significant maintenance issues with roadheaders. This study explores multi-layer dimensionality reduction of vibration signal features in complex environments to enhance the differentiation of different operational states of a roadheader, thereby achieving fault recognition of key components. Concurrently, reducing dimensionality in manifold spaces positively influences operational state differentiation. Therefore, this paper integrates manifold learning to conduct multi-sensor and multi-layer data mining to enhance the differential phenotypes between faults of key components of the roadheader. Initially, we constructed multiple status-reference sample sets for each sensor individually, forming multiple manifolds at different spatial points, and utilizing locality-preserving projections (LPP) to extract low-dimensional manifold features. Further fusion of low-dimensional features from multiple sensors was used to elevate samples, constructing an enhanced spatial pseudo-manifold. Finally, we used LPP to re-reduce the enhanced sensitive feature set from multiple vibration sensors, establishing a dual-layer sensitive feature enhancement learning model. Conducting fault recognition analysis on experimental vibration signals, using k-nearest neighbors (KNN) to classify the enhanced feature set, we achieved a recognition success rate of 98.75% for samples, proving the method’s feasibility in fault recognition under complex loads. Full article

Unmanned aerial vehicles must achieve precise flight maneuvers despite disturbances, parametric uncertainties, modeling inaccuracies, and limitations in onboard sensor information. This paper presents a robust adaptive control for trajectory tracking under nonlinear disturbances. Firstly, parametric and modeling uncertainties are addressed using model reference adaptive control principles to ensure that the dynamics of the aerial vehicle closely follow a reference model. To address the effects of disturbances, a modified nonlinear disturbance observer is designed based on estimated state variables. This observer effectively attenuates constant, nonlinear disturbances with variable frequency and magnitude, and noises. In the next step, a two-stage sliding mode control strategy is introduced, incorporating adaptive laws and a commanded-filter to compute numerical derivatives of the state variables required for control design. An error compensator is integrated into the framework to reduce numerical and computational delays. To address sensor inaccuracies and potential failures, a high-gain observer-based state estimation technique is employed, utilizing the separation principle to incorporate estimated state variables into the control design. Finally, Lyapunov-based stability analysis demonstrates that the system is uniformly ultimately bounded. Numerical simulations on a DJI F450 quadrotor validate the approach’s effectiveness in achieving robust trajectory tracking under disturbances. Full article

Power transmission lines transfer energy between power plants and substations by means of a linear chain of towers. These towers are often situated over extensive distances, sometimes in regions that are difficult to access. Wireless sensor networks present a viable solution for monitoring these long chains of towers due to their wide coverage, ease of installation and cost-effectiveness. The proposed LoRaBUS approach implements and analyses the benefits of a linear topology using a mixture of LoRa and LoRaWAN protocols. This approach is designed to enable automatic detection of nearby nodes, optimise energy consumption and provide a prioritised transmission mode in emergency situations. On remote, hard-to-reach towers, a prototype fire protection system was implemented and tested. The results demonstrate that LoRaBUS creates a self-configurable linear topology which proves advantageous for installation processes, node maintenance and troubleshooting node failures. The discovery process collects data from a neighbourhood to construct the network and to save energy. The network’s autonomous configuration can be completed within approximately 2 min. In addition, energy consumption is effectively reduced 25% by dynamically adjusting the transmission power based on the detected channel quality and the distance to the nearest neighbour nodes. Full article

This work introduces a model for lead-acid battery health monitoring in automobiles, focusing on detecting degradation before complete failure. With the proliferation of electronic modules and increasing power demands in vehicles, along with enhanced sensor data availability, this study aims to investigate battery lifespan. Dead batteries often lead to customer dissatisfaction and additional expenses due to inadequate diagnosis. This study seeks to enhance predictive diagnostics and provide drivers with timely warnings about battery health. The proposed method employs the Detrended Cross-Correlation Analysis Coefficient for end-of-life detection by analyzing the cross-correlation of voltage signals from batteries in different states of health. The results demonstrate that batteries with a good state of health exhibit a coefficient consistently within the statistically significant cross-correlation zone across all time scales, indicating a strong correlation with reference batteries over extended time scales. In contrast, batteries with a deteriorated state of health compute a coefficient below 0.3, often falling within the non-significant cross-correlation zone, confirming a clear decline in correlation. The method effectively distinguishes batteries nearing the end of their useful life, offering a low-computational-cost alternative for real-time battery monitoring in automotive applications. Full article

This paper demonstrates that biodiesel production processes can be optimized through implementing a controller based on fuzzy logic and neural networks. The system dynamics are identified utilizing convolutional neural networks, enabling tests of the reactor temperature response under different control law proposals. In addition, a sensorless technique using a convolutional neural network to replace the sensor/transmitter signal in case of failure is implemented. Two optimization functions are proposed utilizing a metaheuristic algorithm based on differential evolution, where the aim is to minimize the use of cooling for the control of the reactor temperature. Finally, the control system proposals are compared, and the results show that a neuro-fuzzy controller without optimization restrictions generated unviable ITAE ($1.9597\times {10}^7$) and TVU (22.3993) performance metrics, while the restriction proposed in this work managed to minimize these metrics, improving both the ITAE ($3.3928\times {10}^6$) and TVU (17.9132). These results show that combining the sensorless technique and our optimization method for the cooling stage enables energy saving in the temperature control processes required for biodiesel production. Full article

The propeller state of unmanned aerial vehicles (UAV) is difficult to detect in real time due to trouble with laying out the sensor and multiple signal sources. To solve this problem, a fault detection method for multi-rotor UAV propellers was proposed based on a signal analysis of the built-in inertial measurement unit (IMU). Firstly, the multi-source coupled signals of the UAV flight were obtained through the ground station. Then, the picked-up signals were optimally separated according to the multi-rotor UAV propeller fault dynamics model, and signals rich in fault information were obtained. Finally, the separated signals were calculated using the symmetrized dot pattern (SDP), and then the similarity index was used to quantify the distribution of the signal in the feature plot to realize propeller fault detection. The OTSU algorithm was used to quantify the detection results, yielding a similarity of 76.2% in the z-axis direction, which is better than the values in the other two directions. The simulation and experimental analysis of the propeller failure dynamics model showed that the proposed method can effectively identify the propeller faults of multi-rotor UAVs. Full article

The detection of small molecules is critical in many fields, but traditional electrochemical detection methods often exhibit limited accuracy. The construction of multi-mode sensors is a common strategy to improve detection accuracy. However, most existing multi-mode sensors rely on the separate analysis of each mode signal, which can easily lead to sensor failure when the deviation between different mode results is too large. In this study, we propose a multi-mode sensor based on Prussian Blue (PB) for ascorbic acid (AA) detection. We innovatively integrate back-propagation artificial neural networks (BP ANNs) to comprehensively process the three collected signal data sets, which successfully solves the problem of sensor failure caused by the large deviation of signal detection results, and greatly improves the prediction accuracy, detection range, and anti-interference of the sensor. Our findings provide an effective solution for optimizing the data analysis of multi-modal sensors, and show broad application prospects in bioanalysis, clinical diagnosis, and related fields. Full article

This article proposes a fault diagnosis method based on an adaptive sliding mode observer (SMO) for current sensors (CSs) in the charging modules of DC charging piles. Firstly, we establish a model of the phase-shift full-bridge (PSFB) converter with CS faults. Secondly, the fault of the CS is reconstructed through system augmentation and non-singular coordinate transformation. Then, an adaptive SMO is designed to estimate the reconstructed state, and the residual between the actual value of the reconstructed state and the observed value is used as the fault detection variable. Finally, by using norms to design adaptive thresholds and comparing them with fault detection variables, the diagnosis of incipient faults, significant faults, and failure faults in CSs can be achieved. The experimental results verify the effectiveness of the proposed method in this paper; the robustness of the method has been verified under the conditions of DC voltage fluctuations and load fluctuations. Full article

To address the urgent demand for autonomous rapid initial alignment of vehicular inertial navigation systems in complex battlefield environments, this study overcomes the technical limitations of traditional stationary base alignment methods by proposing a robust moving-base autonomous alignment approach based on multi-source information fusion. First, a federal Kalman filter-based multi-sensor fusion architecture is established to effectively integrate odometer, laser Doppler velocimeter, and SINS data, resolving the challenge of autonomous navigation parameter calculation under GNSS-denied conditions. Second, a dual-mode fault diagnosis and isolation mechanism is developed to enable rapid identification of sensor failures and system reconfiguration. Finally, an environmentally adaptive dynamic alignment strategy is proposed, which intelligently selects optimal alignment modes by real-time evaluation of motion characteristics and environmental disturbances, significantly enhancing system adaptability in complex operational scenarios. The experimental results show that the method proposed in this paper can effectively improve the accuracy of vehicle-mounted alignment in motion, achieve accurate identification, effective isolation, and reconstruction of random incidental faults, and improve the adaptability and robustness of the system. This research provides an innovative solution for the rapid deployment of special-purpose vehicles in GNSS-denied environments, while its fault-tolerant mechanisms and adaptive strategies offer critical insights for engineering applications of next-generation intelligent navigation systems. Full article