Preventive Maintenance Research Articles

ResnetCPS for Power Equipment and Defect Detection

Routine visual inspection is fundamental to the preventive maintenance of power equipment. Convolutional neural networks (CNNs) substantially reduce the number of parameters and efficiently extract image features for classification tasks. In the actual production and operation process of substations, due to the limitation of safety distance, camera monitoring, inspection robots, etc., cannot be very close to the target. The operational environment of power equipment leads to scale variations in the main target and thus compromises the performance of conventional models. To address the challenges posed by scale fluctuations in power equipment image datasets, while adhering to the requirements for model efficiency and enhanced inter-channel communication, this paper proposed the ResNet Cross-Layer Parameter Sharing (ResNetCPS) framework. The core idea is that the network output should remain consistent for the same object at different scales. The proposed framework facilitates weight sharing across different layers within the convolutional network, establishing connections between pertinent channels across layers and leveraging the scale invariance inherent in image datasets. Additionally, for substation image processing mainly based on edge devices, smaller models must be used to reduce the expenditure of computing power. The Cross-Layer Parameter Sharing framework not only reduces the overall number of model parameters but also decreases training time. To further enhance the representation of critical features while suppressing less important or redundant ones, an Inserting and Adjacency Attention (IAA) module is designed. This mechanism improves the model’s overall performance by dynamically adjusting the importance of different channels. Experimental results demonstrate that the proposed method significantly enhances network efficiency, reduces the total parameter storage space, and improves training efficiency without sacrificing accuracy. Specifically, models incorporating the Cross-Layer Parameter Sharing module achieved a reduction in the number of parameters and model size by 10% to 30% compared to the baseline models.

A computationally efficient method for induction motor bearing fault detection based on parallel convolutions and semi-supervised GAN

ABSTRACT Accurate and timely bearing fault detection is imperative for optimal system functioning and the implementation of preventative maintenance measures. Deep learning models provide viable solutions to these malfunctions, however, the lack of labelled data makes the training both expensive and cumbersome. To remedy this, various semi-supervised approaches have surfaced in the last decade, significantly mitigating the need for extensive labelled data but with added computational cost. This study proposes one such approach by leveraging generative adversarial networks (GAN) trained on a time-frequency based representation. The proposed Parallel Convolutions Semi-Supervised GAN, namely PC-SSGAN, uses bottleneck parallel convolutions blocks to capture multi-scale features in both local and global contexts, lacing both the generator and discriminator with enhanced feature extraction capabilities and simultaneously reducing the parameters and training time. The Proposed framework is evaluated on two distinct open-source datasets. The classification accuracy for both models exceeded 99.50%. Moreover, the proposed parallel convolutions-based architecture spent approximately 33% less time on training than the normal convolutional layers. It has been foreseen that the proposed fault detection system can be integrated into the motor fault tolerant control system to produce a unified framework that can make informed decisions to handle the bearing faults effectively.

Reliability analysis of DDV and SPM in BOP-based Weibull model and expand fault dataset using deep learning

The hydraulic systems of deepwater Blowout Preventers (BOPs) are crucial for ensuring the reliability and safety of offshore oil and gas extraction operations. A functional failure can lead to uncontrolled blowouts, resulting in casualties and significant economic losses on the rig. The Direct Drive Valve (DDV) and the Subsea Plated Mounted (SPM) valve are key components that help maintain the proper functioning of the hydraulic system in deepwater BOPs. This study begins by utilizing the Weibull analysis method to assess the reliability of the DDV and SPM valves using limited fault data samples. To enhance the accuracy of predictions, Weibull parameters are estimated through various methods, including Maximum Likelihood Estimation (MLE), Least Squares Estimation (LSE), and a combination of Correlation Coefficient Optimization with Support Vector Regression (CCO + SVR).Given the challenges in gathering extensive fault data for DDV and SPM valves—due to complex subsea environments, cost constraints, time limitations, and other factors—this study proposes a method employing a Back Propagation Neural Network (BPNN) model to augment the limited fault data samples. To ensure the reliable operation of the DDV and SPM valves, preventive maintenance cycles are established at 2840 and 7550 operations, respectively. At the same reliability level, as the number of operational cycles increases, the remaining service life of the valves gradually decreases, leading to a higher probability of failure over a shorter timeframe. The Mean Remaining Life (MRL) of the DDV and SPM valves, corresponding to different operational times, is analyzed, providing essential reference points for their usage and maintenance. When the extended data sample is utilized for reliability evaluation, the reliability characteristics of the small fault data samples are effectively reflected, and the parameter prediction error remains low. This indicates that the extended data sample is more suitable for reliability evaluation.

Gas Pipe Leakage Inspection Robot with Real Time Monitoring System

In this Review paper the study focusses on Pipe inspection robots,robots are advanced devices engineered to traverse the interiors of pipelines, assessing conditions and identifying issues like leaks, corrosion, or blockages in industries such as oil and gas, water supply, and sewage. Equipped with high-resolution cameras, sensors, and diagnostic tools, these robots offer detailed, real-time data to support preventive maintenance and reduce manual inspection challenges. Recent developments focus on improving robot agility, compactness, and durability, enabling them to handle varied pipe diameters and complex pathways. Enhanced with wireless connectivity and artificial intelligence, some models can now perform autonomous navigation and defect detection, significantly boosting efficiency and safety. This review examines current innovations, operational hurdles, and future advancements in pipe inspection robotics, with a particular emphasis on smarter navigation systems and sensor technology to enhance inspection accuracy and dependability.

Improving Equipment Maintenance—Switching from Corrective to Preventative Maintenance Strategies

This paper explores different building maintenance strategies in commercial buildings in Sydney, Australia, focusing on corrective maintenance (CM) and preventive maintenance (PM). While CM involves rectifying issues after they occur, PM aims to enhance productivity by anticipating potential issues. Although PM seems more logical, the decision to implement this type of maintenance strategy are typically made based on item reliability, failure frequency, and downtime cost, commonly found in manufacturing facilities or critical environments. However, as found in the selected/surveyed commercial real estate buildings, CM was more frequently adopted in aged facilities with older infrastructure, and PM was favoured for buildings without structural deficiencies; however, operating equipment failures were common. However, in many cases, decision makers did not consider the broader effects of downtime beyond direct financial losses, costs associated with customer satisfaction, worker efficiency, rent abatements, and reputation damage. While each building is unique and may require a bespoke maintenance schedule, this study’s insights may help managers select the most appropriate maintenance strategy. Nonetheless, further research is needed to investigate the role of innovative technologies (such as machine learning and artificial intelligence) in enhancing maintenance efficacy and explore the influences of economic shifts, corporate and financial objectives, and the availability of technical resources.

Approximating the Pareto frontier for bi-objective preventive maintenance and workshop scheduling: A Lagrangean lower bounding methodology for evaluating contracting forms

AbstractEffective planning of preventive maintenance plays an important role in maximizing the operational readiness of any industrial system. We consider an operating system and a maintenance workshop governed by two stakeholders who collaborate based on a mutual contract: components of the operating system that need maintenance are sent to the maintenance workshop, where necessary maintenance activities are performed and after which the maintained components are returned to the operating systems and ready to be used again. While the maintenance activities must obey the workshop capacity, the components should be returned to the operating system within a contracted time frame. For this problem, we developed in a previous work a mixed-integer linear optimization model incorporating stocks of damaged as well as repaired components, workshop scheduling, and preventive maintenance planning for the operating system. We then investigated an availability contract between the stakeholders and which is in the paper at hand compared with a turn-around time contract type, which is more often used in reality. Since, for real instance sizes, the latter leads to a computationally demanding bi-objective optimization problem, we use Lagrangean relaxation and subgradient optimization to compute local lower bounds on the set of non-dominated points, complemented with math-heuristics to identify good feasible solutions (i.e., local upper bounds). Our suggested method thus provides a bounding of the set of non-dominated points for a turn–around time contract.

Increasing the Wear Resistance of the Impellers of Electrical Centrifugal Submersible Pumps Through Repair

Objective: This research investigates the challenges of maintaining electrical submersible pumps (ESP) and develop effective repair techniques to enhance their reliability and efficiency. Theoretical Framework: The research draws upon theories and models related to pump maintenance, materials science, and corrosion prevention. Methodology: A case study approach was adopted, focusing on a Weir VTP submersible pump in continuous operation for 15 years. The pump's condition was assessed, and the root causes of erosion and corrosion were identified. Laser cladding with a TiC coating was applied to the damaged areas. Results and Discussion: The results demonstrated a significant improvement in the pump's wear resistance and overall efficiency following the laser cladding treatment. The TiC coating effectively mitigated the effects of erosion and corrosion, extending the pump's lifespan and reducing maintenance costs. Research Implications: The findings of this research contribute to the development of more effective maintenance strategies for submersible centrifugal pumps. The use of laser cladding with TiC coatings can be applied to other types of pumps and machinery operating in harsh environments. This research also highlights the importance of regular inspections and preventive maintenance. Originality/Value: This study provides a practical solution to the problem of erosion and corrosion in submersible centrifugal pumps. The application of laser cladding with a TiC coating is a relatively novel approach, offering a more effective repair method compared to traditional techniques. The results can be applied to improve the reliability and efficiency of pumps in various industries.

Generative AI for Predictive Maintenance: Predicting Equipment Failures and Optimizing Maintenance Schedules Using AI

Predictive maintenance has emerged as a transformative approach to managing equipment health, reducing unplanned downtime, and extending asset lifespan. Leveraging advancements in generative artificial intelligence (AI), this paper explores the role of AI-driven predictive maintenance in predicting equipment failures and optimizing maintenance schedules. Traditional maintenance strategies, such as reactive and preventive approaches, often lead to inefficiencies, increased operational costs, and unexpected breakdowns. Predictive maintenance, powered by AI, offers a proactive alternative that not only anticipates failures but also enhances scheduling efficiency, maximizing equipment uptime and reducing maintenance costs. Generative AI models, including techniques such as Generative Adversarial Networks (GANs) and reinforcement learning, have shown immense promise in learning complex patterns from historical data and simulating potential equipment failure scenarios. These AI-driven models can analyze vast and diverse data sources—including sensor readings, maintenance logs, environmental conditions, and historical failures—to provide accurate, real-time insights into equipment health. This paper details the architecture and functioning of generative AI models in predictive maintenance, emphasizing their role in both anomaly detection and failure prediction. A systematic comparison of reactive, preventive, and predictive maintenance is provided, underscoring the unique benefits and challenges of predictive maintenance. We discuss the types of data essential for predictive maintenance and present sample data structures used in model training and deployment. Additionally, this paper demonstrates how generative AI models predict equipment failures by identifying anomalous behaviors before they escalate, enabling preemptive actions. A failure probability model is presented to illustrate how failure risks evolve over time, alongside tables showcasing the critical data points in predictive maintenance. The paper also explores the optimization of maintenance schedules using generative AI, where models simulate and compare different maintenance timing strategies, ultimately minimizing downtime and maximizing productivity. However, we also acknowledge the current limitations of generative AI in this domain, including data privacy concerns, computational intensity, and the challenges of model interpretability for practical implementation. Looking forward, we examine future trends such as the integration of Internet of Things (IoT) devices and the emergence of more sophisticated AI models that will likely enhance predictive maintenance applications. This paper concludes by highlighting the transformative potential of generative AI for predictive maintenance, offering insights for industries seeking to innovate their maintenance practices and achieve superior operational resilience.

A novel performance prediction method for harmonic reducers based on the trend-enhanced Fisher’s discriminant ratio-Wiener process

Abstract Harmonic reducers, as core components of industrial robots, play a critical role in maintaining robot health. Performance degradation assessment and remaining useful life (RUL) prediction are essential for ensuring the operational reliability of such robots. To address the multistage characteristics and stochastic uncertainties in the performance degradation of harmonic reducers, a performance prediction method based on Fisher’s discriminant ratio and Wiener process is proposed. Firstly, the health indicator construction is defined as an optimization problem based on Fisher’s discriminant ratio and trend monotonicity constraints. By leveraging the sum of the multiplication of frequency amplitudes and optimized weights, precise segmentation of the multistage degradation process over the entire lifecycle is achieved. Notably, the optimized weights can automatically identify the resonance frequency bands caused by damage. Subsequently, a nonlinear Wiener process model with a drift coefficient in the form of a power function is established. The probability density function expression for RUL is derived based on the concept of first hit time. Additionally, a logarithmic likelihood function for the unknown parameters in the degradation model is constructed. The experimental results indicate that the proposed method surpasses the other two Wiener process models in terms of root mean square error, mean absolute percentage error, and cumulative relative accuracy. This provides robust support for preventive maintenance decision-making for harmonic reducers.

Optimization of Maintenance Strategies in an Oil and Gas Production Facility to Minimize Maintenance Cost

Abstract: In this study, maintenance strategies in an oil and gas production facility were optimized to minimize maintenance costs. The centrifugal pump system in Port Harcourt Refinery was selected for this study because it significantly impacts on the productivity of the refining plant. The pump system’s failure mode effects and criticality analysis (FMECA) were examined, and an optimized maintenance task was developed for the system. The exponential reliability method was employed to analyse the pump’s failure data to determine the pump systems' failure rate and reliability while reliability centred maintenance (RCM) and linear programming (LP) model were employed to optimize the pump’s maintenance strategies and the pump’s maintenance labour force respectively. Broad-based results of the optimized maintenance strategies consisted of on-condition-directed (CD) maintenance, preventive maintenance (PreM), and Proactive maintenance (ProM) strategies to be carried out monthly. The optimized maintenance labour force result revealed that approximately three engineers and two technicians should be employed for the pump’s maintenance task with a minimum of N2, 585, 700 as cost of salaries for the labour force monthly as against the current maintenance labour force requiring fourteen engineers and thirteen technicians monthly. The results showed that the labour cost decreased from N180,000,000 per year to N31,028,400 per year (approximately 82.76% reduction) using the proposed optimized maintenance task compared to the current maintenance plan. Conclusion and recommendation were made that the maintenance optimization technique presented in this work should be adopted by the oil and gas processing and production companies in order to minimize maintenance cost.

Qualitative evaluation and corrective planning for centrifugal pump maintenance at Soekarno-Hatta airport

This study focuses on enhancing the maintenance processes of centrifugal pumps at Soekarno-Hatta Airport’s Water Treatment Unit in Indonesia, crucial for meeting the clean water needs of the airport, which served around 19.8 million passengers in 2022. Using a qualitative methodology, the research involved focus group discussions with the unit’s operators, technicians, and engineers to pinpoint maintenance challenges and devise solutions. Key findings reveal issues such as insufficient routine maintenance, unplanned repairs, and inadequate staffing, leading to operational disruptions and pump failures. The study highlights the role of Total Productive Maintenance (TPM) in reducing machine breakdowns and improving efficiency. It emphasizes the critical role of centrifugal pumps in the airport’s water supply system. The research proposes several corrective measures, adhering to the 5W + 1H framework, including regular lubrication, bearing replacements, hiring more staff, and advanced training on PLC systems. These actions aim to rectify immediate maintenance problems and establish a foundation for the long-term effectiveness of the pump systems. Conclusively, the study underscores the need for a comprehensive maintenance strategy that aligns with standard operating procedures and preventive maintenance. This approach is essential for boosting the operational performance and reliability of the Water Treatment Unit. It has broader implications for similar infrastructure facilities, underscoring the importance of efficient maintenance management.

The Effect of Kuwait’s Climate on the Efficiency of Solar Cells in the Electricity Production Network (A Theoretical Study)

The research aims to demonstrate the climate impacts in Kuwait on the efficiency of solar cells in the electricity production network, and to analyze climate constraints and problems related to the use of solar energy in electricity production. The study specifically aims to demonstrate the impact of high temperatures and the potential effects of sand and sandstorms, and certainly to find potential technical solutions. To increase the efficiency of solar cells in Kuwait’s climatic environment, based on the importance of the topic as it is an addition to engineering studies, as it highlights the benefits of using solar energy in producing electricity, and through the use of the descriptive approach and the analytical approach, several results were reached, the most important of which was, The high temperature under which solar panels operate is one of the most important factors that affect their productivity, and The methods of installing solar cells may affect their exposure to solar radiation, which affects the efficiency of electricity production, The study recommends using smart prediction systems based on AI and programming these systems to operate only when temperatures rise above certain levels to save energy. We also recommend using machine learning to perform preventive and regular maintenance and monitor the performance of solar panels.

Application of the Finite Element Method for Analyzing the Vibration Characteristics of a Spindle System and Fault Diagnosis

The performance of a spindle will gradually decline, the vibration intensity will increase, and the temperature rise will become abnormal with the accumulation of service time. Consequently, the accuracy of the machining product will not meet its production requirements. Studies on the variation characteristics of the spindle unit have clarified the reasons for its abnormal vibration and temperature rise, in principle, to help enterprises conduct preventive maintenance before any serious failure occurs and improve production efficiency. Based on the Timoshenko beam model and rotor dynamics theory, this study uses the finite element method (FEM) to analyze the vibration characteristics of the spindle rotor system. Moreover, it thoroughly analyzes the influence of the spindle bearing heat on the stiffness of the spindle system and identifies the resonance conditions of the spindle rotor within the power frequency range. This research has identified that when the vibration frequency of the spindle operates at a speed of 12,000 r/min, if its vibration frequency is lower than 200 Hz, it will be affected by abnormal vibrations during startup, which will weaken the health status of the spindle and reduce its service life. Similarly, when the working speed of the spindle is 30,000 r/min, if its vibration frequency is lower than 50 Hz, abnormal vibration will occur during startup and operation, thereby reducing its service life.

Self-supervised Learning Approach for Anomaly Detection in Rotating Machinery

Early fault detection in rotating machinery needs careful expert analysis of vibration data for monitoring a component state. Online methods that automatically set a threshold and raise an alarm when the vibration signature is anomalous are meant to efficiently manage key assets in a preventive maintenance plan. In recent years a focus has raised on data driven methods in parallel with the increasing attention towards machine learning and, particularly, deep learning models. In this regard, for rotating equipment components, an important aspect relates to labelled data scarcity for supervised training. On the other hand, the advent of the Internet of Things allows to gather data from multiple assets with relevant information on the asset state itself. Self-supervised learning methods in deep learning application are currently tackling this problem. Investigating Self-learning approaches to integrate domain knowledge and learn relevant features from unlabeled data is therefore important for condition monitoring applications. In this paper a methodology is proposed based on cycle consistency representation learning for training an embedder network on univariate unlabeled data. In order to learn a distance metric in the embedding space the original data are transformed to generate sequences of augmented inputs to enforce learnable pattern similarity in the augmented pairs. A differentiable cycle-consistency loss is chosen to maximize the numbers of augmented pairs in the learned embedding space that have minimum features distance. The pretext task in the described self-supervised setting aims to train a feature extractor for discriminating dissimilar samples in the embedding space by a distance metric and to provide a useful representation for down-stream tasks. The paper analyzes the performance of the approach for anomaly detection in rotating machinery. The methodology is tested on vibration data provided by the Center for Intelligent Maintenance Systems (IMS), University of Cincinnati, considering different accelerated life test campaigns. The data were collected to monitor the fault development in bearings and the model shows how the learned embedding space discriminates effectively anomalous samples from normal ones in the degradation stages of the bearings.

Condition‐based maintenance policy for a multi‐component system considering stochastic dependence and quality loss

AbstractThe reliability evaluation of multi‐component systems has attracted increasing concern in academic and industrial circles. However, the research maintenance optimization for multi‐component systems is still underexplored due to the curse of dimensionality. This paper makes a novel contribution to the existing literature by proposing a condition‐based maintenance (CBM) policy for multi‐component systems with joint consideration of stochastic dependence and quality loss. The system is composed of critical and auxiliary components. The deterioration process of the critical component can be accelerated when the auxiliary one degrades to a certain state. Based on the states of the components, the operation cost of the system owing to quality loss is analyzed, and the operation cost is used as the threshold for the preventive maintenance (PM) decision. Semi‐regenerative technology is used to model the evolution process of the system, and the long‐run average cost is calculated. The optimal inspection period and PM threshold are derived by minimizing the long‐run average cost, and the stationary distribution of the system state under the optimal long‐run average cost is derived with a proposed algorithm. Sensitivity analysis is conducted to reveal the most sensitive parameter. Finally, an illustrative example is presented, and a comparison experiment with other maintenance policies is conducted to demonstrate the effectiveness of the proposed policy.

Reliability Analysis and Preventive Maintenance of Rehabilitation Robots

The complex system structure makes the robot face the problem of frequent failures, which seriously affects the reliability of the device and the safety of the patients, and the reliability analysis and maintenance strategy of the lower limb exoskeleton rehabilitation robot are studied to reduce the occurrence of failures. Existing studies lack a comprehensive assessment and integrated analysis of the whole system, and optimizing the maintenance strategy to minimize the cost under the premise of ensuring system reliability remains an urgent challenge. This study proposes a systematic reliability assessment and preventive maintenance strategy for a lower limb exoskeleton rehabilitation robot. Based on the degradation model, the performance degradation process of each critical component is predicted, and a multi-objective optimization model is constructed by combining three kinds of costs to propose the optimal preventive maintenance strategy. Rehabilitation robot verify the effectiveness of the optimization method.

Algorithmic modelling of a complex redundant multi-state system subject to multiple events, preventive maintenance, loss of units and a multiple vacation policy through a MMAP

Algorithmic modelling of a complex redundant multi-state system subject to multiple events, preventive maintenance, loss of units and a multiple vacation policy through a MMAP

Predictive modeling and estimation of moisture damages in Swedish buildings: A machine learning approach

Identifying potential moisture damage is crucial for maintenance practices and assurance of well-being of occupants. However, due to limited information availability and standardization, assessing damage prevalence on the building stock scale remains understudied. By combining investigation records and building databases, this study leverages data analytic techniques and machine learning modeling to characterize damage pathology and predict its occurrence in Swedish buildings. The interrelationships between damage-specific attributes and their associations with building parameters of several damage types were analyzed using feature selection, forming the basis for developing predictive models. Results show that multilabel classifiers outperform binary classifiers for every damage type, with lead tree ensemble models achieving minimum average AUCPR and F2 of 0.85 for microbial growth, 0.87 for deformation, 0.91 for odor, and 0.95 for water leakage. The identified patterns were interpreted and verified against descriptive statistics. The binary relevance models estimate that one-third of school buildings, 20% of commercial and office buildings, and 15% of residential dwellings in regional building stock contain moisture damage. These findings advance the quantification of moisture damage by providing new knowledge and approaches for appraising moisture damage likelihood at aggregated and individual building levels, thereby aiding in moisture safety evaluations and preventive maintenance efforts.

MANUTENÇÃO PREDIAL: ANÁLISE DAS PRINCIPAIS PATOLOGIAS

This paper addresses the main pathologies in civil construction, focusing on the analysis of causes, effects and solutions for defects that compromise the durability and safety of buildings. The objective of the study is to highlight the importance of preparing detailed projects, executing construction processes in accordance with Brazilian technical standards and using preventive maintenance as essential factors to avoid the emergence of the most recurrent pathologies identified, such as: cracks, fissures, splits, infiltrations, carbonation of concrete and corrosion of reinforcements. As a result, it was observed that the inadequate choice of materials and the lack of qualified labor are indicated as the most significant factors for the emergence of the pathologies listed above. Thus, building maintenance is essential to ensure that structures achieve the planned durability, avoiding high costs with future repairs and ensuring the safety and functionality of buildings.

Monitoring stress-induced brittle rock mass damage for preventative support maintenance

Stress-induced brittle fracturing near an excavation boundary results in a volume increase, known as bulking. Excessive bulking places added demand on the rock support, which, if not detected and addressed through preventative support maintenance (i.e., proactively added reinforcement), can cause the support to fail, leading to a safety hazard and costly production delays for underground mining operations. For caving mines, these project risks are exacerbated during cave establishment due to the large abutment stress from undercutting that redistributes and concentrates stresses near excavations critical for production. This paper reports the findings from research conducted to develop and improve geotechnical monitoring practices to support preventative support maintenance in deep mining operations. This research uses a unique geotechnical monitoring database collected for the Deep Mill Level Zone panel cave mine. The data was collected across a large footprint during the mine's ramp-up period and represents an initial step toward best practices for data collection at cave mines operating in high-stress environments. Borehole camera surveys supplemented by multi-point borehole extensometers have been used to determine the depth of stress fracturing in pillar walls as a function of the distance away from the undercut. Convergence measurements and LiDAR scanning are used to characterize the corresponding rock mass bulking. The results show that the interpretation of monitoring data can be used to identify the long-term depth of stress fracturing and bulking trends in response to undercut advances. These show that direct measures of stress-induced fracturing damage provide an early indication of excavations vulnerable to bulking and that LiDAR scanning is an effective method for capturing the onset of bulking and anticipating local areas likely to experience greater deformation demand as bulking progresses. Proactive and strategic geotechnical monitoring based on the long-term depth of stress-induced fracturing trends is proposed to assist with preventative support maintenance practices.