Performance Degradation Of Equipment Research Articles

Availability-Oriented Maintenance Strategy of Key Equipment in Automated Production Line Considering Performance Degradation

Long-term manufacturing would bring performance degradation of key equipment in automated production lines (APLs), leading to operating unbalance and inefficiency. The existing maintenance strategies can generate maintenance schemes, but they generally ignore performance degradation that can further influence the production process. To efficiently generate maintenance schemes with maximized availability and minimized cost, an availability-oriented maintenance strategy considering performance degradation is proposed based on a synchronized hybrid optimization algorithm (SHEA) in this study. Firstly, the performance degradation of key equipment in APLs is analyzed based on the Weibull distribution, and the availability of the equipment is analyzed considering performance degradation. Secondly, a maintenance model for the key equipment in APLs is established, and a SHEA based on multi-objective particle swarm optimization (MOPSO) and squirrel search algorithm (SSA) is proposed to optimize the maintenance model with higher efficiency. Finally, the effectiveness and superiority of the proposed method are verified by citing a practical APL of the engine cylinder head as an example in the case study.

A Study on the Detection of Fault Factor in Gear-Integrated Bearing

High-precision lasers and anti-aircraft radars are the main equipment to protect the Korean Peninsula, and require preemptive maintenance before signs of failure. Of the key components in the drive sector, bearings do not have a fault alarm function. Therefore, the technology for diagnosing defects in bearings before the performance degradation of equipment occurs is becoming more important. In this paper, for the experimental analysis, we measured the acceleration of the four sets of the same lot using acceleration sensors. Through periodic measurements, the factors that changed until the bearing stopped rotating were analyzed. To determine the replacement time, the main factors and threshold values of the bearing signal were analyzed. The error of the theoretical and experimental analysis results of the defect frequency was within 2.8 %, and the validity of the theoretical analysis results could be confirmed. Based on the results, it is possible to remotely transmit trouble alerts to users through the system check function.

Analysis of Heat Source System Degradation Due to Aging and Evaluation of Its Effect on Energy Consumption

The performance of air conditioning systems deteriorate due to the natural aging and wear caused by operating the devices. This is termed “aging degradation,” and it results from a lack of appropriate maintenance which accelerates the degree of performance degradation. The performance degradation of an air conditioning system can cause problems such as increased energy consumption, deteriorated indoor heating environment, and shortened lifespan of air conditioning equipment. To prevent such problems, it is important to establish a long-term maintenance plan to recover degraded performance, such as predicting an appropriate maintenance time by identifying the real-time performance degradation rate based on a system’s operation data. In this study, the performance degradation rate, according to the operating time, was estimated using long-term operation data for devices constituting a heat source system, and the effect of performance degradation of the heat source system’s operation and energy consumption was reviewed using a simulation. The performance degradation rate of the target device was estimated by analyzing the variation trend of the calibration coefficient, which was calculated when the initial performance prediction model was calibrated through operating data. Using this approach, it was confirmed that the annual performance degradation rate was 1.0–1.4% for the heat source equipment, 0.4–1.2% for the cooling towers, and 0.8–1.3% for the pumps. In addition, a heat source system energy simulation calculated the 15-year performance degradation of the heat source equipment to be 34–52% and 7–19% for both the cooling towers and pumps. Due to the equipment performance deterioration, the number of operating heat source equipment and cooling tower fans, and the pump flow rate gradually increased every year, thus accelerating the performance deterioration even further. As a result, energy consumption in the 15th year increased by approximately 41% compared with the initial energy consumption.

A Convolutional Neural Network-Based Recognition Method of Gear Performance Degradation Mode

Abstract In an increasingly intelligent modern society, whether in industrial production activities or daily life, mechanical transmission equipment is more and more widely used. Once a failure occurs, it will not only cause the stagnation of industrial production, bring huge economic losses and environmental pollution, but may also cause casualties. Therefore, it is particularly important to identify and monitor the performance degradation of mechanical equipment. Based on the convolutional neural network (CNN), a stacking incremental deformable residual block network recognition model is proposed. This method converts the one-dimensional signal recognition problem into an image recognition problem. The average pooling layer replaces the fully connected layer, and the large-size convolution kernel is replaced with a small-size convolution kernel. With the recognition of the gear performance degradation modes, the experiment proves that the multi-channel recognition model has a better recognition effect.

Bayesian Calibration of Performance Degradation in a Gas Turbine-Driven Compressor Unit for Prognosis Health Management

Abstract Prognosis health management is an effective way to improve the operational safety and economy of industrial equipment. The development of an accurate and quick response model to monitor equipment health status, predict performance, and diagnose faults is key to its implementation. However, the inevitable performance degradation of industrial equipment over time poses a significant challenge to such a model. In this work, we adopt a Bayesian approach to calibrate thermodynamic simulations with time-dependent parameters that account for performance degradation. The relationship between degradation and time is modeled through an assumed functional form, referred to as a health indicator. The proposed health indicator calibration method gives a rapid assessment of degraded equipment performance and elucidates how degradation relates to time. The novelty of this paper is that it regards performance degradation as an uncertainty quantification problem rather than a deterministic problem. The health indicator calibration method is validated on a natural gas compressor and a gas turbine. The results show that when severe degradation occurs, functional calibration improves predictive performance over nonfunctional calibration (i.e., independent of time). The introduced method provides valuable decision support to extend the service life and reduce maintenance costs for industrial equipment. Its feedback in operation can also develop the service assessment criteria and inform the design of subsequent generations of industrial equipment.

Remining Useful Life Prediction of Equipment Driven by Multi-Sensor Data

This paper presents a method for predicting the remining useful life (RUL) of equipment by using multi-sensor monitoring data of equipment. Firstly, the performance degradation of equipment is evaluated based on the maximum information coefficient. Then the performance degradation model is built by using Wiener process with non-linear drift. The maximum likelihood is used to estimate the parameters of the model, and the probability density distribution of RUL considering random failure threshold is deduced. Finally, the parameters of the model are updated by improved particle filter, and the RUL of the remaining equipment is predicted.

Research on Prediction Method of Performance Degradation of Flexible Optoelectronic Film Material Processing Equipment Based on Adaptive Fuzzy Clustering

Flexible photoelectric film is an anisotropic material. The slight change of equipment performance during processing is prone to cause deformation of the material. Therefore, it is important to predict the degradation of processing equipment performance. Since the performance degradation of flexible photoelectric film material Roll-to-Roll (R2R) processing equipment is a nonlinear process, this paper introduces an adaptive fuzzy clustering method to construct a fuzzy membership function model for calculating the performance degradation index of R2R processing equipment and studies the parameter solving method such as the AFCM division of the roller vibration data, the category center value of the fuzzy membership function, and the input data division area width. Finally, the performance degradation index calculation algorithm is designed. The roller shaft accelerated life test was carried out using self-made equipment. The test data were 1000 sets. The results showed that the root mean square eigenvalues and the kurtosis eigenvalues of the roller vibration data are sensitive to the performance degradation. The equipment performance curve described by the first and second types of performance degradation indicators was very stable in the early stage. After the 800th group, the curve continued to decrease, and the change was more severe, indicating that the performance degradation of the equipment is more serious. In the 980th group, the longer-lasting roller shaft was damaged, and the performance index value was about zero, which proved the correctness of the performance degradation prediction method proposed in this paper in calculating the performance degradation value of the equipment.

Method of Health Performance Evaluation and Fault Prognostics for Electronic Equipment

To deal with the health performance degradation of electronic equipment, a new health evaluation method based on improved manifold learning algorithm and HMM is proposed in this paper. Firstly, according to SNPP algorithm KSUNPP is proposed by introducing an uncorrelated constraint and kernel method, and the improved algorithm is used for feature extraction. Secondly, the health evaluation model of electronic equipment is constructed. Then, by calculating KL distance which can measure the fault degradation, the model can evaluate the health performance degradation. Finally, the proposed method is applied to health evaluation of electronic equipment which belongs to one type of missile, experiment results demonstrate that the method is effective.

Investigation of power frequency magnetic field radiation in typical high-rise building

Busway risers and power cables in high-rise buildings normally carry a relatively large current to deliver power to different levels of the building, and this raises a concern on both the human safety issue and the performance degradation of magnetic sensitive office equipment due to the generated large magnetic field radiation. In this paper, the impact and situation of magnetic field radiations at power frequency inside high-rise buildings is reviewed, and followed by a case study of magnetic field assessment inside a typical high-rise building in Hong Kong. Copyright © 2011 John Wiley & Sons, Ltd.

Suppression of electromagnetic radiation noise from wireless modules in the millimeter‐wave band by means of alumina containing carbon black

AbstractUnwanted electromagnetic (EM) radiation generated from wireless modules in the millimeter‐wave band used for car radar creates errors and results in performance degradation of electronic equipment. In this paper, to reduce these unwanted EM waves, an optimal design to add the function of EM wave absorption to the lid for the module is examined. Alumina‐containing carbon black is used as a wave absorption material for the lid. Absorption of 20 dB or more is obtained as a measured result with the cover added for EM wave absorption at 60 GHz. The wireless module was also analyzed in the millimeter‐wave band with the designed material used for the lid. The radiation and the reflection of electric power were greatly reduced by the loss electric power from the lid. Therefore, effective data for suppressing unwanted EM radiation noise from wireless modules in the millimeter‐wave band can be provided. © 2010 Wiley Periodicals, Inc. Electron Comm Jpn, 93(10): 25–33, 2010; Published online in Wiley Online Library (wileyonlinelibrary.com). DOI 10.1002/ecj.10216 3

A Study on Performance Degradation of Digital Electronic Equipment under Electromagnetic Disturbance

In this research, the performance degradation of the digital electronic equipment under electromagnetic (EM) disturbance was studied in order to investigate the interference of intra-equipment. To develop the evaluation method of the performance degradation, some communication indexes were measured under EM disturbance. From some experimental results, it is known that the performance degradation of the electronic equipment was estimated by the degradation of through-put, one of the communication performance indexes. For further investigation of the interference of intra-equipment, the near EM field from a PCB of the electronic equipment and its performance degradation under EM disturbance were measured and compared. From the measured results, the relationship between near field measurement and performance degradation could be obtained in some extent. These facts enable us that the weak area under the EM disturbance application on PCB can be foreseen by measuring the near field emission from the equipment and vise versa.