Design of a Condition Monitoring System for Wind Turbines

Renewable energy is being adopted worldwide, and the proportion of offshore wind turbines is increasing. Offshore wind turbines operate in harsh weather conditions, resulting in various failures and high maintenance costs. In this paper, a condition diagnosis model for condition monitoring of an offshore wind turbine has been developed. The generator, main bearing, pitch system, and yaw system were selected as components subject to the condition monitoring by considering the failure rate and downtime of the wind turbine. The condition diagnosis model works by comparing real-time and predictive operating data of the wind turbine, and about four years of Supervisory Control and Data Acquisition (SCADA) data from a 2 MW wind turbine was used to develop the model. A deep neural network and an artificial neural network were used as machine learning to predict the operational data in the condition diagnosis model, and a confusion matrix was used to measure the accuracy of the failure determination. As a result of the condition monitoring derived by inputting SCADA data to the designed system, it was possible to maintain the failure determination accuracy of more than 90%. The proposed condition monitoring system will be effectively utilized for the maintenance of wind turbines.

Research on Engineering Geomechanics Characteristics and CFRP Reinforcement Technology Based on Machine Learning Algorithms

We have completed the design of an early warning and evaluation analysis module based on machine learning algorithms. Aiming at the prestressed CFRP-strengthened reinforced concrete bridges under natural exposure, we developed a theoretical model to analyze the long-term prestress loss of reinforced parts and the adhesion behavior of the CFRP-concrete interface under natural exposure conditions. The analysis deeply reveals the technical and engineering geomechanics characteristics of the D bridge. At the same time, through a series of experimental studies on the D bridge condition monitoring system, the data acquisition and transmission, processing and control of the D bridge condition monitoring system, and the bridge condition monitoring and evaluation software are provided. Regarding how to repair the engineering geomechanical characteristics of D bridge, we mentioned the prestressed CFRP reinforcement technology. The prestressed carbon fiber reinforced composite (CFRP) structure made of reinforced concrete (RC) makes better use of the high-strength characteristics of CFRP and changes. It strengthens the stress distribution of the components and improves the overall strength of the components. It is more supported by engineers in the civil engineering and transportation departments. However, most prestressed CFRP-reinforced RC structures are located in natural exposure environments, and the effect of natural exposure environments on the long-term mechanical properties of prestressed C FRP-reinforced RC components is still unclear. This article mainly uses the research on the engineering geomechanics characteristics and reinforcement technology of the bridge body, so that people have a deep understanding of its concept, and provides reasonable use methods and measures for the maintenance and protection of the bridge body in the future. This paper studies the characteristics of engineering geomechanics based on machine learning algorithms and applies them to the research of CFRP reinforcement technology, aiming to promote its better development.

Optimization of an autonomous robotic drilling system for the machining of aluminum aerospace alloys

This paper aims to identify the capability of a highly flexible industrial robot modified with a high-speed machine spindle for drilling of aluminum 6061-T6. With a focus on drilling feed rate, spindle speed, and pecking cycle, the hole surface roughness and exit burr heights were investigated using the Taguchi design methodology. A state of the art condition monitoring system was used to identify the vibrations experienced during drilling operation and to establish which robot pose had increased stiffness, and thus the optimum workspace for drilling. When benchmarked against a CNC machine the results show that the CNC was capable of producing the best surface finish and the lowest burr heights. However, the robot system matched and outperformed the CNC in several experiments and there is much scope for further optimization of the process. By identifying the optimum pose for drilling together with the idealized settings, the proposed drilling system is shown to be far more flexible than a CNC milling machine and when considering the optimized drilling of aerospace aluminum this robotic solution has the potential to drastically improve productivity.

Proposed Smart Monitoring System for the Detection of Bee Swarming

This paper presents a bee-condition-monitoring system incorporated with a deep-learning process to detect bee swarming. This system includes easy-to-use image acquisition and various end node approaches for either on-site or cloud-based mechanisms. This system also incorporates a new smart CNN engine called Swarm-engine for detecting bees and the issue of notifications in cases of bee swarming conditions to the apiarists. First, this paper presents the authors’ proposed implementation system architecture and end node versions that put it to the test. Then, several pre-trained networks of the authors’ proposed CNN Swarm-engine were also validated to detect bee-clustering events that may lead to swarming. Finally, their accuracy and performance towards detection were evaluated using both cloud cores and embedded ARM devices on parts of the system’s different end-node implementations.

A Method for Mill Monitoring Based on Inter-insert Periodic Correlation Using Singular Value Decomposition (SVD)

This paper introduces a method to monitor the wear of end milling tools in real-time production based on inter-insert periodic correlation. The aim is to detect abnormal behavior of the cutter as early as possible to prevent severe tool failure and subsequent losses. The approach takes advantage of the angular domain to segment the signal in periodic cycles of the same angular duration, which are then amenable to correlation analysis. An ordered separability index with latent correlation characteristics is proposed to assess the current operating state of the tool. A series of simulations with existing experimental data are run to test the feasibility of the proposed index and to calculate the corresponding confidence interval. This approach has a high potential to form an efficient tool condition monitoring system. Compared to the traditional teach-in method, this method is more independent of the cutting conditions (changes of velocity or direction) and has no requirement for a trial cut, making the method useful for small batch production and can reduce the rate of false alarms.

Automated Condition Monitoring with Remaining Lifetime Assessment for Wire Ropes in Ladle Cranes

The hazards and deterioration of operating wire ropes on overhead cranes, which articulate the ladle in the basic oxygen steelmaking process and are subjected to intensive periodic loads and exposure to high temperatures, are discussed. An automated condition monitoring system (ACMS) based on a magnetic flux leakage testing (MFL) flaw detector permanently installed on the rope under test is used. An algorithm of the rope’s residual tensile strength assessment is provided. A specially developed software that submits a decision on the rope’s condition to the crane operator is described. The practice of combining magnetic rope testing (MRT) and tensile strength analysis for the quantitative assessment of rope condition is reviewed. Practical issues are also discussed, such as how to establish the condition monitoring process, set loss thresholds for rope metallic cross-sectional area, and safely prolong the service life of rope.