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The failure modes of products gradually show a diversified trend with the precision and complexity of the product structure. The combination of fault tree analysis and generalized grey relational analysis is widely used in the fault diagnosis of complex systems. In this study, we utilize a method that combines fault tree analysis and generalized grey relational analysis. This method is applied to diagnose the Expansion Adhesive Debonding fault of automobile doors. Then, we analyse and compare the differences in actual fault diagnosis results. The comparison involves three analysis methods: Fault Tree Analysis combined with Absolute Grey Relation Analysis (F-AGRA), Fault Tree Analysis combined with Relative Grey Relation Analysis (F-RGRA), and Fault Tree Analysis combined with Comprehensive Grey Relation Analysis (F-CGRA). Subsequently, we compare the findings with actual production results. This comparison allows us to discuss the differences between the three methods in the fault diagnosis of complex systems. We also discuss the application occasions of these methods. This study will provide a new method for fault analysis and fault diagnosis in the actual production of the automobile manufacturing industry. This method can eliminate faults effectively and accurately and improve product quality and productivity.

The state of health (SOH) and remaining useful life (RUL) of lithium-ion batteries are two important factors which are normally predicted using the battery capacity. However, it is difficult to directly measure the capacity of lithium-ion batteries for online applications. In this paper, indirect health indicators (IHIs) are extracted from the curves of voltage, current, and temperature in the process of charging and discharging lithium-ion batteries, which respond to the battery capacity degradation process. A few reasonable indicators are selected as the inputs of SOH prediction by the grey relation analysis method. The short-term SOH prediction is carried out by combining the Gaussian process regression (GPR) method with probability predictions. Then, considering that there is a certain mapping relationship between SOH and RUL, three IHIs and the present SOH value are utilized to predict RUL of lithium-ion batteries through the GPR model. The results show that the proposed method has high prediction accuracy.

Battery state of health (SOH) estimating is essential for the safety and preservation of electric vehicles. The degradation mechanism of batteries under different aging conditions has attracted considerable attention in SOH prediction. In this article, the discharge voltage curve early in the cycle is considered to be strongly characteristic during cell aging. Therefore, the battery aging state can be quantitatively characterized by an incremental capacity analysis (ICA) of the voltage distribution. Due to the interference of vibration noise of the test platform, the discrete wavelet transform (DWT) methods are accustomed to soften the premier incremental capacity curves in different hierarchical decompositions. By analyzing the battery aging mechanism, the peak of the curve and its corresponding voltage are used in the characterization of capacity decay by grey relation analysis (GRA) and to optimize the input of the deep learning model, and finally, the double-layer long short-term memory network (LSTM) model is used to train the data. The results demonstrate that the proposed model can predict the SOH of a single battery cycle using only small batch data and the relative error is less than 2%. Further, by freezing the LSTM layer for transfer learning, it can be used for battery health estimation in different loading modes. The results of training and verification show that this method has high accuracy and reliability in SOH estimation.

Global competition and increasing environmental concerns have compelled manufacturing industries to reduce energy consumption and enhance product quality. This, in turn, helps increase the production rate. In this context, the machining performance is largely influenced by the selection of process parameters and the condition of the cutting tool. The present study is based on an experiment involving the use of an uncoated, deep cryogenically treated tungsten carbide tool for machining AISI 1045 steel. The outcomes were evaluated using Grey Relational Analysis (GRA) and the Preference Selection Index (PSI). Both ANOVA methods indicated that feed rate, cutting speed, the use of deep cryo-treated tools, and depth of cut had the most significant effects. The optimal parameter settings identified include a deep cryo-treated tool, a cutting speed of 120 m/min, a feed rate of 0.05 mm/rev, and a depth of cut of 1.00 mm. This approach demonstrated that the feed rate had the greatest influence on flank wear and surface roughness, both of which were also significantly affected by cutting speed and depth of cut. Moreover, the deep cryo-treated tool outperformed the untreated tool, resulting in reductions in surface roughness and flank wear by 17% and 7%, respectively. Deep Cryogenic Treatment (DCT) has thus shown promise in enhancing the performance of tungsten carbide cutting tools used in machining operations. This study specifically investigated the effect of DCT on tool wear and surface finish during the turning of AISI 1045 steel.

In the present work welding of pure aluminium and pure copper is carried out employing friction welding process. The selected process parameters in the investigation are spindle speed, friction force, upset force and burn off. The evaluated responses are tensile strength and total shrinkage. Single and multi-objective optimization is carried out to understand the significance of process parameters. Design of experiments and ANOVA analysis is carried out in single objective optimization and Taguchi based Grey relational analysis is carried out for multi objective optimization. Regression equations are generated in either cases to interpret the relation between selected process parameters and responses. Comparison of optimized process parameters of tensile strength and total shrinkage for single objective optimization and multi objective optimization is carried out and presented to showcase the process parameter importance in obtaining the required response.

Dry machining processes frequently encounter challenges, including increased cutting forces, high friction, and poor surface finishes, primarily due to the absence of lubrication. To address these issues, this study introduces the development of solid-lubricating cutting tools (SLTs) by incorporating elements such as Nichrome, silver, molybdenum, strontium sulfate, and calcium fluoride into a Zirconia Toughened Alumina matrix. The objective was to enhance the tribological performance of cutting tools for turning AISI 4340 steel under dry conditions. An experimental design based on the L8 mixed orthogonal array was employed, and the Grey-Taguchi analysis method was used to optimize multiple performance measures, including cutting forces, coefficient of friction, and surface roughness. Among the fabricated tools, SLT 4 exhibited superior performance. The results demonstrated a 73% reduction in cutting forces, a 45% decrease in the coefficient of friction, and a 66% improvement in surface finish compared to the unmodified base tool. These enhancements were attributed to the formation of a stable self-lubricating layer on the tool surface during machining. A confirmation experiment validated the optimization outcomes, confirming the effectiveness of the proposed tool composition.

To reduce the impact of volatility on photovoltaic (PV) power generation forecasting and achieve improved forecasting accuracy, this article provides an in-depth analysis of the characteristics of PV power outputs under typical weather conditions. The trend of PV power generation and the similarity between simultaneous outputs are found, and a hybrid prediction model based on feature matching, singular spectrum analysis (SSA) and a long short-term memory (LSTM) network is proposed. In this paper, correlation analysis is used to verify the trend of PV power generation; the similarity between forecasting days and historical meteorological data is calculated through grey relation analysis; and similar generated PV power levels are searched for phase feature matching. The input time series is decomposed by singular spectrum analysis; the trend component, oscillation component and noise component are extracted; and principal component analysis and reconstruction are carried out on each component. Then, an LSTM network prediction model is established for the reconstructed subsequences, and the external feature input is controlled to compare the obtained prediction results. Finally, the model performance is evaluated through the data of a PV power plant in a certain area. The experimental results prove that the SSA-LSTM model has the best prediction performance.

Based on the previous research on the preparation of solid waste ceramsite (SW-ceramsite), the phosphorus removal performance from aqueous solution by SW-ceramsite in a fixed-bed column was investigated. Characterization results showed that the S BET , pH pzc , pore volume, bulk density and void fraction values of pretreated SW-ceramsite were 4.18 × 10 4 cm 2 /g, 9.83, 3.05 × 10 3 cm 3 /g, 1.37 g/cm 3 and 69.8%, respectively. Column experiments indicated that under optimal operating conditions of an initial pH of 5, an initial phosphorus concentration of 5 mg/L, a reaction temperature of 323 K, and an initial flowrate of 40 mL/min, the breakthrough curve (BTC) exhibited an irregular “S” shape, and the breakthrough and saturation times were 80 h and 155 h, respectively. Kinetic analysis demonstrated that compared with the Adams-Bohart model, the Yoon-Nelson model better described the phosphorus removal behavior from water by SW-ceramsite in a fixed-bed column. Grey relation analysis (GRA) results suggested that except for initial flowrate, the assumed effects of initial pH, initial concentration, and reaction temperature on the BTC were consistent with the GRA outcomes, implying that the GRA method can be used to determine the relative influence of the aforementioned factors on the phosphorus removal performance from water in a fixed-bed column packed with SW-ceramsite. Furthermore, after eight regeneration cycles, the breakthrough and saturation times of SW-ceramsite packing decreased by 30% and 12.9%, respectively, suggesting that it has a certain regenerative ability.

The production of a single gas well is influenced by many geological and completion factors. The aim of this paper is to build a production prediction model based on machine learning technique and identify the most important factor for production. Firstly, around 159 horizontal wells were collected, targeting the Duvernay Formation with detailed geological and completion records. Secondly, the key factors were selected using grey relation analysis and Pearson correlation. Then, three statistical models were built through multiple linear regression (MLR), support vector regression (SVR), gaussian process regression (GPR). The model inputs include fluid volume, proppant amount, cluster counts, stage counts, total horizontal lateral length, gas saturation, total organic carbon content, condensate-gas ratio. The model performance was assessed by root mean squared errors (RMSE) and R-squared value. Finally, sensitivity analysis was applied based on best performance model. The analysis shows following conclusions: (1) GPR model shows the best performance with the highest R-squared value and the lowest RMSE. In the testing set, the model shows a R-squared of 0.8 with a RMSE of 280.54 × 104 m3 in the prediction of cumulative gas production within 1st 6 producing months and gives a R-squared of 0.83 with a RMSE of 1884.3 t in the prediction of cumulative oil production within 1st 6 producing months (2) Sensitivity analysis based on GPR model indicates that condensate-gas ratio, fluid volume, and total organic carbon content are the most important features to cumulative oil production within 1st 6 producing months. Fluid volume, Stages, and total organic carbon content are the most significant factors to cumulative gas production within 1st 6 producing months. The analysis progress and results developed in this study will assist companies to build prediction models and figure out which factors control well performance.

In recent years, Transformer-based methods have shown full potential in power load forecasting problems. However, their computational cost is high, while it is difficult to capture the global characteristics of the time series. When the forecasting time length is long, the overall shift of the forecasting trend often occurs. Therefore, this paper proposes a gray relation analysis–frequency-enhanced decomposition transformer (GRA-FEDformer) method for forecasting power loads in power systems. Firstly, considering the impact of different weather factors on power loads, the correlation between various factors and power loads was analyzed using the GRA method to screen out the high-correlation factors as model inputs. Secondly, a frequency decomposition method for long short-time-scale components was utilized. Its combination with the transformer-based model can give the deep learning model an ability to simultaneously capture the fluctuating behavior of the short time scale and the overall trend of changes in the long time scale in power loads. The experimental results show that the proposed method had better forecasting performance than the other methods for a one-year dataset in a region of Morocco. In particular, the advantages of the proposed method were more obvious in the forecasting task with a longer forecasting length.