Equipment Performance Degradation Research Articles

A multi-phase mission success evaluation approach for maritime autonomous surface ships considering equipment performance degradation and system composition changes

With the development of Maritime Autonomous Surface Ships (MASSs), the mission success assessment problem cannot be ignored because of potential security issues of MASSs. This paper addresses the issue of performance degradation at different stages, the problem of state dependence in multi-stage missions of MASSs, and the correlation problem between missions of stages. Based on the coupling of the reliability model of each single-stage mission system, a multi-stage mission success evaluation method for MASSs is proposed. By leveraging the ability of the conditional probability tables in Bayesian networks to express the complex relationships between the nodes, the dynamic Bayesian network model of stages is constructed based on the fault tree. Based on the Markov process, the problem of state dependence on shared equipment between stages is solved. Considering the complex relationship between multi-stage missions, the virtual node is introduced, and the multi-state Bayesian network is combined to realize the coupling of the reliability evaluation results of each single-stage mission. It is applied to the multi-stage mission success evaluation of the MASS to obtain the success probability of MASSs and the key equipment of each stage. The results show that evaluation results are more suitable for engineering practice.

Resilience-oriented adaptive predictive maintenance optimization for continuous process manufacturing systems considering mission profile variation

Continuous process manufacturing systems (CPMSs) are typical phased mission systems that require high standards of operational stability, reliability, and safety. With the variation of production mission profiles, CPMSs are required to run in diverse modes or conditions in different phases; therefore, to meet these standards, maintenance decisions applied to CPMSs should be adapted to such variations. Considering that the concept of “resilience” provides a systematic solution to evaluate system adaptability via “disruption absorption” and “recoverability,” this paper proposes a CPMS resilience evaluation model and utilizes it as guidance for the optimization of CPMS predictive maintenance (PdM). The proposed method consists of the following steps: (1) applying a customized Seasonal Trend Decomposition model to predict the future trend of production mission profile variations, (2) assessing the production mission accomplishment capability of CPMS based on a Gamma process model of equipment performance degradation, (3) using disruption response ratio to evaluate CPMS resilience based on mission accomplishment capability, and (4) proposing a Simulated Annealing Q-Learning algorithm for adaptive PdM optimization, which keeps resilience above a threshold level while minimizing maintenance costs. The applicability and effectiveness of the proposed method are validated by an industrial case study of a nuclear fuel rod shielding component CPMS.

THEORETICAL FRAMEWORKS OF ECOPFM PREDICTIVE MAINTENANCE (ECOPFM) PREDICTIVE MAINTENANCE SYSTEM

The Frameworks of EcoPFM Predictive Maintenance (PM) System presents a novel approach to maintenance optimization within eco-friendly power facilities, addressing the critical need for sustainable, efficient asset management. This paper introduces an integrated framework leveraging advanced predictive analytics, machine learning algorithms, and Internet of Things (IoT) technology to enable proactive maintenance interventions based on real-time data insights. Focusing on the context of the United States it highlights the significance of implementing such a system in the realm of eco-friendly energy infrastructure. The automotive and heavy-duty truck industries in the United States grapple with the challenge of optimizing maintenance strategies to ensure vehicle reliability, safety, and environmental sustainability. Traditional maintenance approaches, primarily reactive or scheduled maintenance, fall short in addressing the complexities of modern vehicle operations. The U.S. Department of Transportation reports that heavy-duty trucks transport approximately 70% of the nation's freight by weight, underscoring the sector's critical role in the economy. However, inefficiencies in maintenance strategies contribute to significant economic and operational setbacks. According to the American Transportation Research Institute, unscheduled truck maintenance and repairs are leading operational costs for fleets, with an average expense of 16.7 cents per mile in 2020, highlighting the financial strain of current maintenance practices. In the United States, the demand for eco-friendly power solutions is rapidly increasing, driven by a growing awareness of environmental sustainability and the imperative to reduce carbon emissions. As the nation transitions towards renewable energy sources and eco-friendly power facilities, the effective management of these assets becomes paramount to ensuring reliability, performance, and longevity. The EcoPFM PM System integrates diverse data sets sourced from eco-friendly power facilities across the USA, encompassing historical operational data, sensor readings, and environmental parameters. Through predictive analytics, the system identifies patterns and trends within these data sets to forecast equipment failures and performance degradation accurately. By prioritizing maintenance tasks based on risk assessment models and condition monitoring, the system enables organizations to optimize resource allocation, minimize downtime, and extend asset lifespan. Embracing the Frameworks of EcoPFM Predictive Maintenance System holds immense promise for organizations operating eco-friendly power facilities in the United States. By harnessing data-driven insights and proactive maintenance strategies, this system offers a pathway towards enhanced operational efficiency, cost reduction, and sustainability, ultimately contributing to the advancement of eco-friendly energy infrastructure in the nation.
 Keywords: Predictive Maintenance, System, ECOPFM, Technology.

Advanced data analytics modeling for evidence-based data center energy management

Over the past few decades, the demand for Data Center (DC) services has significantly increased due to the world’s growing need for internet access, social networking, and data storage. Data Centers are among the most energy-intensive businesses, so optimizing IT operations in DC requires energy-efficient techniques. This paper presents AI based modeling strategies for effective energy management with a particular emphasis on DC’s two most energy intensive systems (i.e., cooling and IT systems). This study addresses the issues of IT equipment performance degradation, inappropriate IT room thermal conditions, inefficient workload placement, and excessive energy waste. This research entails the application of machine learning for DC thermal classification, and deployment of deep learning models to predict resource utilization and energy consumption in DC. Furthermore, a comparative analysis is performed with existing relevant methods to demonstrate the effectiveness and accuracy of the proposed AI techniques. The findings of this study also provide evidence-based recommendations for DC efficient energy management.

Mechanical Equipment Performance Degradation Assessment Method Based on Improved Degradation Feature Extraction and Sample Completeness

Performance degradation assessment (PDA), as an important part of health management, is playing a crucial role in evaluating the degenerate state of mechanical equipment. To evaluate the performance degradation state more effectively by using monitoring data of mechanical equipment, a PDA method based on improved degradation feature extraction technology and considering sample completeness is proposed in this paper. Firstly, multiple degradation features, including statistical features and intrinsic energy features, are extracted to construct a high-dimensional feature set calculated by time-domain analysis and the EMD method. Then, a sensitive feature set, which has significant robustness, correlation, and monotonicity in the degenerate process, is reduced and processed by the improved PCA method to obtain the final health index. Secondly, the degradation assessment model is established by considering the completeness of the samples. As for the complete sample dataset, which has both normal and failure state data, a logistic regression model (LRM) is built to assess the performance degradation status. In addition, an SVDD model is established for the incomplete sample dataset, which only has normal state data. Finally, the reliability of the proposed method is verified by the public XJTU-SY dataset, and the comparative experiment further verifies the superiority of this method.

Review on the Application of the Nonlinear Output Frequency Response Functions to Mechanical Fault Diagnosis

Key components of mechanical equipment are prone to defects or failures during operation, resulting in equipment performance degradation because of harsh working environments and abnormal working conditions. The faults may induce the mechanical structures to behave nonlinearity. Some linear methods cannot accurately describe systems’ nonlinear characteristics. Nonlinear output frequency response functions (NOFRFs) well reveal the generation mechanism of nonlinear phenomena. The main objective of this article is to summarize the development and state-of-the-art research of NOFRFs in mechanical fault diagnosis. The method of NOFRFs is briefly introduced. Associated indexes of NOFRFs are summarized and two case analyses on the performances of associated indexes are conducted. Recent applications of NOFRFs in mechanical fault diagnosis are analyzed from the following aspects: 1) direct diagnosis of NOFRFs; 2) diagnosis of NOFRFs-associated indexes; and 3) NOFRFs+ diagnosis. Finally, the limitation of NOFRFs in fault diagnosis is pointed out and future directions are given.

Tolerance analysis method considering multifactor coupling based on the Jacobian–torsor model

Assembly quality affects the performance of mechanical products and reasonable tolerance designs are central to assembly quality. Unreasonable tolerance design leads to the mechanical equipment performance degradation under complex operating conditions. This study developed a novel tolerance analysis method that considers the influence of temperature, load, and other environmental factors based on the Jacobian–torsor and skin models. First, the deformation caused by multifactor coupling was analyzed using the finite element method, and the feature surface of deformation is extracted. Second, the deformation extraction and tolerance conversion based on the new generation geometrical product specification theory was used to convert the deformation of the feature into the dimension, orientation, and form tolerance. Third, the modified Jacobian–torsor model considering multifactor coupling was constructed by superimposing the converted tolerance with the design tolerances. Finally, the abrasion problem of the airborne actuator was considered as an example to verify the effectiveness and accuracy of the novel method. Through the accurate calculation of tolerances, it accurately analyzes the assembly state of the product under the influence of environmental factors, and provides more accurate guidance for the design and optimization of product tolerance in complex working conditions.

CURRICULUM DESIGN OF VIRTUAL SIMULATION EXPERIMENT OF THERMISTOR TEMPERATURE MEASUREMENT CHARACTERISTICS BASED ON EDUCATIONAL PSYCHOLOGY

Abstract Background In order to meet the social demand for applied and innovative talents and meet the “advanced, innovative and challenging” university education standard of the Ministry of education, colleges and universities continue to explore new training modes and teaching forms. High quality experimental teaching plays an important role in cultivating students' practical ability and innovative ability. Thermistor is an important category of sensors. Through experimental operation, students can effectively understand and master its physical structure, working principle and application characteristics. Subject and Method In order to overcome the disadvantage of limited resources of traditional thermistor equipment, the design of this virtual simulation experiment follows the principle of “maximum authenticity, combination of virtualization and authenticity”. From the perspective of educational psychology, taking the temperature control link of central heating and air supply system as the background, the experimental steps are designed around the thermistor, and the students are guided to carry out the temperature measurement characteristic experiment of thermistor in combination with the knowledge points such as PID control algorithm and single chip microcomputer data processing. Each step of the experiment is designed to test students' mastery of corresponding knowledge and skills. In addition, by scientifically simplifying and virtualizing each link of the gas supply system, on the premise of ensuring the integrity of the process and the authenticity of the experience, the experimental steps outside the scope of the “sensor” course are still “achievable” for the experimenter. Form the whole process of communication before, during and after the experiment to effectively explain knowledge to students. Before the experiment, students should learn the theoretical knowledge, precautions and operation skills required by the experiment; During the experiment, students should understand information, make plans and collect data; After the experiment, students and teachers make full use of the scattered time segments to communicate on the problems in the experiment. Results Students can query the technical data, choose the design scheme, observe the experimental results, deepen the understanding of thermistor knowledge, improve the engineering practice ability, and get rid of the limitations of time and space in the process of repeatedly “making mistakes” and “correcting mistakes”. The project can completely copy a system through virtual instrument. No physical heat source, no equipment performance degradation, no special safety control requirements, and no restrictions on equipment resources, site and time. It can effectively solve the problems that thermistor is easy to cause performance attenuation, precision decline and affect the effect of experimental teaching. Conclusion After long-term use, the experimental design will not lead to the decline of component performance and material consumption. The experimenter can remotely complete the experimental operation anytime and anywhere through the computer connected to the network, so as to obtain the sensory experience close to the actual operation of the experimental equipment. In short, we should carry out relevant combination of industry and education according to our own advantages and characteristics. Make clear the goal of motivation by example, and regularly hold activities such as successful model sharing, experience introduction and project display, so as to set a successful example for positive emotions, clarify the specific objectives of the activities, form a strong psychological momentum, stimulate internal potential and help the realization of positive behavior. We should strengthen professional guidance, give full play to the concentration of professionals, high intelligence, professional knowledge in business fields, and give full play to the advantages of talents and intelligence. We can organize experts and scholars to establish a professional bid winning guidance team, give full play to the business projects of experts and scholars in their respective fields, and carry out scientific research on relevant projects. Acknowledgements Supported by a project grant from Ministry of Education Collaborative Education Project (202102343015).

Mechatronics Equipment Performance Degradation Assessment Using Limited and Unlabeled Data

Advanced mechatronics equipment requires reliable and effective performance degradation assessment to guarantee long-term operations. Current data-driven predictions endow the operation and maintenance of mechatronic equipment flexibly and intelligently. However, the sufficient and labeled data in real industrial scenes may not be satisfied, resulting in negative impacts of overfitting and time-consuming annotations. In this article, we propose a novel prognostic model, namely unsupervised meta gated recurrent unit (UMGRU) containing a dual-cycle learning architecture with the designed clustering assignment module to deal with few-shot prognostics under unlabeled historical data. It integrates the strength of double gradient based optimizations for abstracting general degradation knowledge and offering a sensitive model status for precisely online adaptation with limited on-site data. Besides, mini-batch pseudolabels are automatically assigned within each inner cycle learning and further participate in parameter upgrades. Finally, both experimental and industrial data are used to verify the effectiveness of UMGRU.

Research on Feature Extraction of Performance Degradation for Flexible Material R2R Processing Roller Based on PCA

Performance feature extraction is the primary problem in equipment performance degradation assessment. To handle the problem of high-dimensional performance characterization and complexity of calculating the performance indicators in flexible material roll-to-roll processing, this paper proposes a PCA method for extracting the degradation characteristic of roll shaft. Based on the analysis of the performance influencing factors of flexible material roll-to-roll processing roller, a principal component analysis extraction model was constructed. The original feature parameter matrix composed of 10-dimensional feature parameters such as time domain, frequency domain, and time-frequency domain vibration signal of the roll shaft was established; then, we obtained a new feature parameter matrix Z org ∗ by normalizing the original feature parameter matrix. The correlation measure between every two parameters in the matrix Z org ∗ was used as the eigenvalue to establish the covariance matrix of the performance degradation feature parameters. The Jacobi iteration method was introduced to derive the algorithm for solving eigenvalue and eigenvector of the covariance matrix. Finally, using the eigenvalue cumulative contribution rate as the screening rule, we linearly weighted and fused the eigenvectors and derived the feature principal component matrix F of the processing roller vibration signal. Experiments showed that the initially obtained, 10-dimensional features of the processing rollers’ vibration signals, such as average, root mean square, kurtosis index, centroid frequency, root mean square of frequency, standard deviation of frequency, and energy of the intrinsic mode function component, can be expressed by 3-dimensional principal components F 1 , F 2 , and F 3 . The vibration signal features reduction dimension was realized, and F 1 , F 2 , and F 3 contain 98.9% of the original vibration signal data, further illustrating that the method has high precision in feature parameters’ extraction and the advantage of eliminating the correlation between feature parameters and reducing the workload selecting feature parameters.

Residual Lifetime Prediction with Multistage Stochastic Degradation for Equipment

Residual useful lifetime (RUL) prediction plays a key role of failure prediction and health management (PHM) in equipment. Aiming at the problems of residual life prediction without comprehensively considering multistage and individual differences in equipment performance degradation at present, we explore a prediction model that can fit the multistage random performance degradation. Degradation modeling is based on the random Wiener process. Moreover, according to the degradation monitoring data of the same batch of equipment, we apply the expectation maximization (EM) algorithm to estimate the prior distribution of the model. The real-time remaining life distribution of the equipment is acquired by merging prior information of real-time degradation data and historical degradation monitoring data. The accuracy of the proposed model is demonstrated by analyzing a practical case of metalized film capacitors, and the result shows that a better RUL estimation accuracy can be provided by our model compared with existing models.

Exploring Equipment Electrocardiogram Mechanism for Performance Degradation Monitoring in Smart Manufacturing

Similar to the use of electrocardiogram (ECG) for monitoring heartbeat, this article proposes an equipment electrocardiogram (EECG) mechanism based on fine-grained collection of data during the entire operating duration of the manufacturing equipment, with the purpose of the EECG to reveal the equipment performance degradation in smart manufacturing. First, the system architecture of EECG in smart manufacturing is constructed, and the EECG mechanism is explored, including the granular division of the duration of the production process, the matching strategy for process sequences, and several important working characteristics (e.g., baseline, tolerance, and hotspot). Next, the automatic production line EECG (APL-EECG) is deployed, to optimize the cycle time of the production process and to monitor the performance decay of the equipment online. Finally, the performance of the APL-EECG was validated using a laboratory production line. The experimental results have shown that the APL-EECG can monitor the performance degradation of the equipment in real-time and can improve the production efficiency of the production line. Compared with a previous factory information system, the APL-EECG has shown more accurate and more comprehensive understanding in terms of data for the production process. The EECG mechanism contributes to both equipment fault tracking and optimization of production process. In the long run, APL-EECG can identify potential failures and provide assistance in for preventive maintenance of the equipment.

A two-layer fuzzy synthetic strategy for operational performance assessment of an industrial hydrocracking process

The operating performance may deteriorate from the optimal condition in industrial hydrocracking process due to changes in product requirements, equipment performance degradation, environmental noises, and random disturbances, etc. Therefore, it is necessary to carry out effective online operating performance assessment for it. In this paper, a two-layer fuzzy synthetic strategy is proposed for operating performance assessment of the hydrocracking process. First, an operating performance assessment index is constructed based on the concept of positive deviation of the quality variables. As there are many products in the process, an assessment system is further built based on the performance index. To calculate the indicators in the assessment index system in real-time, partial least squares is used for prediction. To solve the problem of limited labeled samples in the prediction, the process variables are classified into several subspace blocks according to their correlations with different key indicators. Then, a two-layer fuzzy synthetic evaluation strategy is designed to evaluate the actual operating performance of the hydrocracking process. The proposed strategy is finally illustrated with a series of cases on an industrial hydrocracking process in China.

Multi-state data-driven gas path analysis method

Abstract Data-driven gas path analysis is a current research topic for energy fields for it can provide continuous monitoring to ensure safe and reliable operation of energy systems like gas turbine engines, wind turbines and distributed energy systems. The method utilizes information delivered by sensors to track equipment performance degradation during operation. In recent years, the rapid development of novel energy technologies promotes the progress of the method, but it also brings challenges. Energy systems are now becoming more and more complicated with strong nonlinear performance, highly coupled components and complex control laws. At present, an effective and universal diagnostic method to deal with energy systems with highly nonlinear dynamic performance has not been found. In this paper, a method called multi-state gas path analysis method is put forward to address the problem. The core idea of the method is to create sub-models to extend data available for modelling and diagnosing. The method integrates topological data analysis and transfer learning to construct a sub-model diagnostic network for nonlinear modelling. It allows designers to deal with highly nonlinear dynamic systems while preventing prohibitive computation effort. The method has been applied to an engine platform and verified. It achieves the same 92% diagnostic accuracy with only half data acquisition cost compared to a traditional data-driven gas path analysis method.

Bearing condition degradation assessment based on basic scale entropy and Gath-Geva fuzzy clustering

A method based on basic scale entropy and Gath-Geva fuzzy clustering is proposed in order to solve the issue of bearing degradation condition recognition. The evolution rule of basic scale entropy for bearing in performance degradation process is analyzed first, and the monotonicity and sensitivity of basic scale entropy are emphasized. Considering the continuity of the bearing degradation condition at the time scale, three-dimensional degradation eigenvectors are constructed including basic scale entropy, root mean square, and degradation time, and then, Gath-Geva fuzzy clustering method is used to divide different conditions in performance degradation process, thus realizing performance degradation recognition for bearing. Bearing whole lifetime data from IEEE PHM 2012 is adopted in application and discussion, and fuzzy c-means clustering and Gustafson–Kessel clustering algorithms are analyzed for comparison. The results show that the proposed basic scale entropy-Gath-Geva method has better clustering effect and higher time aggregation than the other two algorithms and is able to provide an effective way for mechanical equipment performance degradation recognition.

Equipment remaining useful life prediction oriented symbiotic simulation driven by real-time degradation data

As an emerging simulation technology in the field of system modeling and simulation, the equipment symbiotic simulation has become research emphasis. In the field of equipment maintenance support, the outstanding problem of equipment remaining useful life (RUL) prediction is analyzed, i.e., the stable model parameters without self-evolution ability, which has become the primary factor that hinders self-adaptive prediction of equipment RUL. Combined with parallel systems theory, the equipment RUL prediction oriented symbiotic simulation framework is proposed on the basis of modeling analysis and Wiener state space model (SSM) is taken as the basic simulation model in the framework. Driven by the dynamic injected equipment degradation observation data, the model parameters are updated online by using expectation maximum (EM) algorithm and the data assimilation between simulation outputs and observation data is executed by using Kalman filter, so as to realize dynamic evolution of the simulation model. The simulation model evolution which makes the simulation outputs close to equipment real degradation state provides high fidelity model and data for predicting equipment RUL accurately. The framework is verified by the performance degradation data of a bearing. The simulation results show that the symbiotic simulation method can accurately simulate the equipment performance degradation process and the self-adaptive prediction of equipment RUL is realized on the basis of improving prediction accuracy, proving the feasibility and effectiveness of symbiotic simulation method.

Conditions and development case studies for mountainous deposits in Siberia

The article contains the materials on deposits development intensification under challenging climatic and mining conditions, including mountainous areas of Siberia. The exploitation case studies for mountainous deposits all over the world and in Russia have been described. The authors have been set out the factors impeding the development of such deposits, and the extent of mining and transportation equipment performance degradation is also indicated. There have been stated the characteristics and the description of one of the newly mountainous gold ore deposits in Siberia which is being developed at an altitude of 2684m. A number of specific factors concerning its development have also been introduced as well as the description of mining technologies engineered by Irkutsk National Research Technical University (IRNRTU) specialists. The depth and principal dimensions of the open pit together with the mining and transportation equipment and facilities have been justified. The prime cost analysis of mineral extraction has been made, which results showed the substantial growth in expenditures for the transportation of the overburden rocks and ores. In view of the above mentioned research, there appeared the necessity for the search of new and the enhancement of current transport vehicles and communications.

Comparison of the LTE Performance Parameters in Different Environments under Close Loop Spatial Multiplexing (CLSM) Mode in Downlink LTE-A

LTE-A present deployment strategies are likely to overcome the User Equipment (UE) performance degradation, in certain parts of the topology incorporated with coverage block holes, due to the presence of high rise buildings. It also promises to serve the increase of high user density, which otherwise results in capacity exhaustion of the macro cells. LTE-A addresses these challenges with new features such as small cell and femto cell in its recent releases. Small cell is a low-cost alternative to macro cell in rural and hard-to-reach areas and its installation at strategic points overcomes the capacity and coverage problem. In contrast, Heterogeneous network with its ubiquitous coverage can deliver high-speed data. In this paper, several LTE Key performance indicators e.g. average UE throughput, average area throughput, spectral efficiency, fairness index are analyzed between small cell network and Heterogeneous network in close loop spatial multiplexing mode (CLSM). Besides, we also emphasize on the point to find out the best suited network architecture for overpopulated and densely urban region under proportional fair scheduling algorithm.

Reliability assessment of the vertical roller mill based on ARIMA and multi-observation HMM

AbstractOnline running condition monitoring of the vertical roller mill (VRM) is significant to assess the equipment performance degradation and reliability. This paper proposes a performance reliability assessment method based on autoregressive integrated moving average (ARIMA) model and hidden Markov model (HMM) using the real-time sensing monitoring signals, which is designed to analyze the running state and predict the reliability of VRM. As most faults of VRM relate to hydraulic pressure of loading system and mechanical vibration, research on hydraulic monitoring and vibration monitoring is prerequisites, which determines the sensing parameters and monitoring points, provides the data base for following reliability assessment. Then ARIMA is applied to establish the performance degradation path using the historical sensing monitoring data. Finally, the multi-observation HMM is used to estimate the reliability changing trend of the equipment, the input observations of which are the predictive data from...

Online residual useful life prediction of large-size slewing bearings—A data fusion method

To decrease breakdown time and improve machine operation reliability, accurate residual useful life (RUL) prediction has been playing a critical role in condition based monitoring. A data fusion method was proposed to achieve online RUL prediction of slewing bearings, which consisted of a reliability based RUL prediction model and a data driven failure rate (FR) estimation model. Firstly, an RUL prediction model was developed based on modified Weibull distribution to build the relationship between RUL and FR. Secondly, principal component analysis (PCA) was introduced to process multi-dimensional life-cycle vibration signals, and continuous squared prediction error (CSPE) and its time-domain features were employed as equipment performance degradation features. Afterwards, an FR estimation model was established on basis of the degradation features and relevant FRs using simplified fuzzy adaptive resonance theory map (SFAM) neural network. Consequently, real-time FR of equipment can be obtained through FR estimation model, and then accurate RUL can be calculated through the RUL prediction model. Results of a slewing bearing life test show that CSPE is an effective indicator of performance degradation process of slewing bearings, and that by combining actual load condition and real-time monitored data, the calculation time is reduced by 87.3% and the accuracy is increased by 0.11%, which provides a potential for online RUL prediction of slewing bearings and other various machineries.