Maintenance Strategies Research Articles

Designing tailored maintenance strategies for systematic reviews and clinical practice guidelines using the Portfolio Maintenance by Test-Treatment (POMBYTT) framework

BackgroundOrganizations face diverse contexts and requirements when updating and maintaining their portfolio, or pool, of systematic reviews or clinical practice guidelines they need to manage. We aimed to develop a comprehensive, theoretical framework that might enable the design and tailoring of maintenance strategies for portfolios containing systematic reviews and guidelines.MethodsWe employed a conceptual approach combined with a literature review. Components of the diagnostic test-treatment pathway used in clinical healthcare were transferred to develop a framework specifically for systematic review and guideline portfolio maintenance strategies.ResultsWe developed the Portfolio Maintenance by Test-Treatment (POMBYTT) framework comprising diagnosis, staging, management, and monitoring components. To illustrate the framework’s components and their elements, we provided examples from both a clinical healthcare test-treatment pathway and a clinical practice guideline maintenance scenario. Additionally, our literature review provided possible examples for the elements in the framework, such as detection variables, detection tests, and detection thresholds. We furthermore provide three example strategies using the framework, of which one was based on living recommendations strategies.ConclusionsThe developed framework might support the design of maintenance strategies that could contain multiple options besides updating to manage a portfolio (e.g. withdrawing and archiving), even in the absence of the target condition. By making different choices for variables, tests, test protocols, indications, management options, and monitoring, organizations might tailor their maintenance strategy to suit specific contexts and needs. The framework’s elements could potentially aid in the design by being explicit about the operational aspects of maintenance strategies. This might also be helpful for end-users and other stakeholders of systematic reviews and clinical practice guidelines.

Optimal Mesh Pore Size Combined with Periodic Air Mass Load (AML) for Effective Operation of a Self-Forming Dynamic Membrane BioReactor (SFD MBR) for Sustainable Treatment of Municipal Wastewater

A self-forming dynamic membrane bioreactor (SFD MBR) is a cost-effective alternative to conventional MBR, in which the synthetic membrane is replaced by a “cake layer,” an accumulation of the biological suspension over a surface of inert, low-cost support originated by filtration itself. Under optimized conditions, the cake layer is easy to remove and quick to form again, resulting a “dynamic membrane.” The permeate of the SFD MBR has chemo-physical characteristics comparable to those of conventional ultrafiltration-based MBR. In this paper, two nylon meshes with pore sizes of 20 and 50 µm, respectively, were tested in a bench-scale SFD MBR in which an air mass load (AML) was periodically supplied tangentially to the filtration surface to maintain filtration effectiveness. The SFD MBR equipped with 20 µm nylon mesh coupled with 5 min of AML every 4 h showed the best performance, ensuring both a permeate with turbidity values always below 3 NTU and revealing no increases in transmembrane pressure (TMP) with manual maintenance needs. A benchmark test with the only difference of a suction break (relaxation) instead of AML was conducted under identical operating conditions for validation with an already known maintenance strategy. This latter test produced a permeate of very good quality, but it needed frequent TMP increases and consequent manual cleanings, showing that a periodic AML coupled with the use of a 20 µm mesh can be an optimal strategy for long-term operation of SFD MBR.

Multi-Objective Optimization for Sustainable Pavement Maintenance Decision Making by Integrating Pavement Image Segmentation and TOPSIS Methods

To provide a low-carbon economy maintenance strategy is the most challenging problem faced by pavement management authorities under the restricted budget and significant environmental repercussions. The development of a multi-objective optimization model for pavement maintenance decision making is essential to formulate pavements. Nevertheless, the existing automatic detection can only recognize and classify pavement distress. However, few studies are able to accurately determine the precise dimensions of specific distresses such as cracks and potholes, especially combined with the actual size of the image. This limitation hinders the ability to provide specific maintenance recommendations and make optimal maintenance decisions. Therefore, this paper develops a comprehensive and effective multi-objective decision-making framework for pavement maintenance. This framework consists of four distinct components: (1) recognizing the dimensions of pavement distresses based on the pavement image segmentation technique; (2) compiling a list of viable pavement maintenance strategies; (3) assessing the costs and carbon emissions of these strategies; and (4) optimizing decisions on pavement maintenance. We used the U-Net algorithm to accurately recognize the dimensions of pavement distresses, while an improved entropy-weighted TOPSIS model was proposed to determine the optimal pavement maintenance strategy with the lowest cost and carbon emissions. The results indicated that the pavement distress dimension recognition model achieved a high accuracy of 96.88%, and the TOPSIS model identified the optimal maintenance strategy with a score of 99.16. This maintenance strategy achieved a substantial reduction of 30.80% in carbon emissions and a cost reduction of 20.81% compared to the highest values among all maintenance strategies. This study not only provides a scientifically objective method for making pavement maintenance decisions but also offers specific, quantifiable maintenance programs, marking a stride towards more environmentally friendly and cost-effective road maintenance. It also contributes to the sustainability of pavement maintenance.

Strategy use and its evolvement in word list learning: a replication study.

Spontaneous strategy employment is important for memory performance, but systematic research on strategy use and within-task evolvement is limited. This online study aimed to replicate three main findings by Waris and colleagues in Quarterly Journal of Experimental Psychology (2021): in word-list learning, spontaneous strategy use (1) predicts better task performance, (2) stabilizes along the task, and (3) increases during the first two task blocks. We administered a shortened version of their original real-word list-learning task to 209 neurotypical adults. Their first finding was partly replicated: manipulation strategies (grouping, visualization, association, narrative, other strategy) but not maintenance strategies (rehearsal/repetition, selective focus) were associated with superior word recall. The second finding on the decrease in strategy changers over task blocks was replicated. The third finding turned out to be misguided: neither our nor the original study showed task-initial increase in strategy use in the real-word learning condition. Our results confirm the important role of spontaneous strategies in understanding memory performance and the existence of task-initial dynamics in strategy employment. They support the general conclusions by Waris and colleagues: task demands can trigger strategy use even in a familiar task like learning a list of common words, and evolution of strategy use during a memory task reflects cognitive skill learning.

Adapting geometry-based polygonal contacts for simulating faulty rolling bearing dynamics

Rolling bearings are a leading cause of equipment breakdowns in electrical machines, underscoring the significance of predictive maintenance strategies. However, the given methods require high-quality big data, which is challenging to acquire, especially for faulty cases. Simulation models offer an alternative by generating large datasets to complement experimental data. However, bearings involve complex contact-related phenomena, such as slipping and clearance. Therefore, generating realistic data comparable to the real-world necessitates accuracy. Our study presents a multibody simulation system of a motor bearing, incorporating a geometry-based polygonal contact method (PCM), which accurately captures nonlinear bearing dynamics and allows for the simulation of various contact geometries. We introduce a systematic approach to adjust the PCM contact properties for rolling bearings, referencing the well-established Hertzian theory. Both healthy and faulty bearings with a local outer ring fault were simulated. The simulated output was a relative shaft displacement, experimentally validated using a capacitive sensor. Our model successfully demonstrates the potential to employ geometry-based contacts for generating realistic data on faulty bearings with the aim of predictive maintenance.

Enhancing Maintenance Services Quality: A Study of Student Satisfaction and Maintenance Strategies in Student’s Residential College

Maintenance services play a pivotal role in maintaining existing facilities, structures, buildings, and real property, encompassing routine maintenance, repairs, and replacement. In the contemporary landscape, the quality of maintenance has emerged as a critical factor in ensuring the optimal function of buildings and their components. This research endeavors to ascertain the perceived levels of students’ satisfaction and expectation towards the quality of maintenance services at the residential college of Universiti Tun Hussein Onn Malaysia Pagoh Campus. This study also aimed to delineate maintenance strategies that leverage several technologies to enhance maintenance service quality. Through questionnaires, this study collected responses from a sample of participants and employed SERVQUAL approach, comprising five distinct elements, for analysis. Findings indicated that students' expectation and satisfaction levels, particularly in the realms of reliability, tangibles, and assurance, yielded high scores. However, responsiveness and empathy recorded comparatively lower scores. Additionally, the Importance Index (I) analysis measured the significance of maintenance strategies and revealed that all identified strategies were not only essential but also of paramount importance. The primary contribution of this study lies in the implementation of SERVQUAL model, which explained the relationship between maintenance service quality and various strategies employed. Recognizing the pivotal role of maintenance service quality in influencing student productivity, this study provides valuable insights for public institutions to enhance their maintenance services. The institutions can effectively elevate maintenance service quality towards advanced technology by emphasizing strategies that align with the five SERVQUAL elements.

Assessment of physical condition and status of maintenance of public primary schools infrastructure in Port Harcourt metropolis

This study presents a comprehensive assessment of the physical condition and maintenance status of public primary school infrastructure in Port Harcourt Metropolis. The investigation delves into various aspects, including the establishment and age of schools, building components, furniture conditions, security infrastructure, basic amenities, and environmental elements. The majority of schools were established after 1960, with a notable concentration of building construction between 2010-2020, indicating recent infrastructural development. While essential components such as classrooms, administrative offices, auditoriums, libraries, and ICT facilities are universally present, variations in the conditions of roofs, ceilings, and electricity supply from the national grid emphasize the need for targeted maintenance efforts. The study identifies a commendable commitment to transparency and accountability, as evidenced by the reporting of infrastructure conditions to higher authorities. However, a significant percentage reporting no major maintenance in the past 5 years raises concerns about the longevity of infrastructure. Recommendations include prioritizing maintenance on critical components, strategic investments in security infrastructure, and fostering sustainability through ongoing maintenance practices. The findings underscore the importance of a multifaceted approach to infrastructure management, aiming for a conducive and sustainable learning environment in public primary schools. The commitment to transparency, coupled with proactive maintenance strategies, is essential for fostering an environment conducive to effective teaching and learning, ultimately contributing to the overall well-being of the educational system in Port Harcourt Metropolis.

Condition-based opportunistic maintenance strategy for multi-component wind turbines by using stochastic differential equations

The components of wind turbines are complex in structure and the working environment is harsh, which makes wind turbines face problems such as high failure rates and high maintenance costs. In this paper, the stochastic differential equation model has been established for the harsh operating environment of wind turbines, and used Brownian motion to simulate random disturbances; aiming at the problem of high failure rate of wind turbines, based on Weibull distribution, a new model has been established by combining operating time and equipment state to calculate the failure rate; in the analysis of monitoring data, the Higher-Order Moment method and Bayesian method were used to solve the parameters. The opportunity maintenance threshold curve and preventive maintenance threshold curve were obtained by analyzing Time-Based Maintenance and Condition-Based Maintenance. Therefore, the Condition-Based Opportunistic Maintenance strategy was obtained. The effectiveness of the proposed method was finally verified by arithmetic examples.

Age Estimation of Transmission Line Using Statistical Health Index and Failure Probability Curve-Fitting Method

The aging process of transmission lines has a direct impact on the reliability and safety of the power grid. Therefore, an accurate age estimation method is imperative for effective maintenance planning and infrastructure investment. This paper introduces a systematic methodology for estimating the age of overhead transmission lines, utilizing the percentage statistical health index (%SHI) and the failure probability curve-fitting (FPCF) method. The %SHI, employing a scoring and weighting approach derived from test results and inspections, is used to assess the actual condition of transmission line equipment. Additionally, the FPCF approach is applied to illustrate the connection between the SHI and the likelihood of failure, facilitating the assessment of transmission line age by fitting failure probability curves to the SHI data. This age is directly associated with the probability of experiencing a failure. The evaluation was conducted on 924 towers situated along four transmission lines connecting the 115 kV substations S1–S2, S3–S4, S5–S6, and S7–S8. These transmission lines are in four regions with diverse terrain and environments such as mountains, rice fields, and more. In the SHI calculation, practical testing results and historical failure data were applied. The results clearly indicate that there were notable disparities in the age estimations for transmission lines in diverse geographical regions of Thailand when compared to their actual ages. These discrepancies can be attributed to various factors, including the local environment, such as rainfall, flooding, and salt-laden air as well as specific geographical features like mountainous and coastal terrain. To mitigate the deterioration of transmission lines in all regions, it is essential to implement a proactive maintenance strategy. This strategy should involve more frequent inspections, the use of advanced monitoring technologies, and the establishment of robust maintenance procedures, with which it would become possible to enhance the accuracy of equipment condition assessments, ultimately resulting in an overall improvement in the reliability of transmission lines.

A complex system health state assessment method with reference value optimization for interpretable BRB

Health condition assessment is the basis for formulating and optimizing maintenance strategies of complex systems, which is crucial for ensuring the safe and stable operation of these systems. In complex system health condition assessment, it is not only necessary for the model to handle various uncertainties to ensure the accuracy of assessment results, but also to have a transparent and reasonable assessment process and interpretable, traceable assessment results. belief rule base (BRB) has been widely used as an interpretable modeling method in health condition assessment. However, BRB-based models currently face two issues: (1) inaccuracies in expert-provided parameters that can affect the model's accuracy, and (2) after model optimization, interpretability may be reduced. Therefore, this paper proposes a new method for complex system health condition assessment called interpretable BRB with reference value optimization (I-BRB). Firstly, to address the issue of inaccurate reference values, a reference value optimization algorithm with interpretability constraints is designed, which optimizes the reference values without compromising expert knowledge. Secondly, the remaining parameters are optimized using the projection covariance matrix adaptation evolution strategy (P-CMA-ES) with interpretability constraints to improve the model's accuracy. Finally, a case study evaluating the bearing components of a flywheel system is conducted to validate the proposed method. Experimental results demonstrate that I-BRB achieves higher accuracy in health condition assessment.

P962 Clinical and endoscopic remission at 3 months after Acute Severe Ulcerative Colitis is predictive of maintenance therapy outcomes at 12 months - Results from PREDICT UC

Abstract Background The optimal maintenance regimen after rescue therapy for Acute Severe Ulcerative Colitis (ASUC) is unknown. We aimed to evaluate prospectively the impact of different maintenance regimens after successful infliximab (IFX) rescue, along with factors associated with Month 12 remission. Methods Clinical responders at 3 months from PREDICT UC, a randomised trial (NCT02770040) comparing standard and intensified IFX rescue strategies, were evaluated in this prospective cohort study up to Month 12. Maintenance therapies were as follows: (1) Thiopurine monotherapy (azathioprine or mercaptopurine) for thiopurine naïve patients or prior suboptimal thiopurine therapy (TP); (2) Combination thiopurine and IFX therapy 5mg/kg 8-weekly for thiopurine-experienced patients (IFX+TP combo); (3) IFX monotherapy 5mg/kg 8-weekly for thiopurine-intolerant patients (IFX mono). All patients received oral 5-aminosalicylates. Clinical and endoscopic assessments were performed at baseline (Month 3) and Month 12. Clinical remission was defined as a partial Mayo score of 0-1 and endoscopic remission as Mayo Endoscopic Score (MES) of 0-1. Results Of 138 patients randomised, 79 entered the maintenance phase. Of these, 56 (71%) received TP, 16 (20%) received IFX+TP combo and 7 (9%) received IFX mono. Baseline characteristics were similar across the 3 maintenance groups. At 12 months, 42 patients (53%) were in clinical remission, 44 patients (56%) were in endoscopic remission, and one patient underwent colectomy. The 3 maintenance groups had similar rates of clinical remission (TP 55%, IFX+TP combo 56%, IFX mono 29%; P=0.39) and endoscopic remission (TP 55%, IFX+TP combo 56%, IFX mono 57%; P=0.99) at 12 months. Clinical Remission On univariable and multivariable analysis, no factors were associated with clinical remission at 12 months, including baseline CRP, albumin, partial Mayo score, MES, UCEIS or maintenance strategy. Endoscopic Remission Factors associated with endoscopic remission at 12 months included baseline partial Mayo score of 0 (OR 3.36, 95% CI 1.24-9.10, P=0.017), MES of 0-1 (OR 3.48, 95% CI 1.08-11.27, P=0.037) and UCEIS of 0-1 (OR 3.30, 95% CI 1.24-8.76, P=0.016). However, a partial Mayo score cut-off of 0-1 did not reach significance (OR 3.46, P=0.09). No factors associated with endoscopic remission were identified on multivariable analysis. Conclusion In steroid-refractory ASUC patients who received IFX rescue, clinical and endoscopic remission at Month 3 were associated with endoscopic remission at Month 12, particularly among patients with greater depth of remission. Month 3 assessment helps identify patients at higher risk of disease progression for whom more intensive treatment may be required.

Evaluation of the Performance of Nature-Based Constructed Wetlands for Treating Wastewater from Various Land Uses in Korea

Land use changes, specifically the growth of impervious areas due to urbanization, exacerbate non-point-source pollutants in stormwater runoff, surpassing discharge from point sources in Korea. The application of nature-based solutions, such as constructed wetlands (CWs), is becoming popular for stormwater treatment, but challenges arise when background concentrations are overlooked, leading to reduced pollutant removal efficiency. This study aims to propose a plan for the sustainability of CWs by evaluating design appropriateness and utilizing existing monitoring results. The evaluation of 63 CWs reveals that meteorological factors, specifically antecedent dry days and rainfall depth, have significant impacts on urban stormwater runoff quality in various land uses, affecting the performance of CWs. Designing CWs considering land use is crucial due to the considerable concentration variations across different land uses. Improving CW performance requires proper maintenance strategies to ensure effective pollutant removal mechanisms, especially for poorly degradable organic substances post treatment. Rainfall characteristics play a pivotal role in CW design and operation, affecting capacity, efficiency estimation, and maintenance frequency. Considering various factors such as land use, watershed characteristics, and ease of maintenance is essential when utilizing CWs. This study’s findings contribute to the design and operation of future CWs, emphasizing the need for continuous performance analyses through long-term monitoring.

Universalism versus residualism: a micro-simulation of alternative income maintenance schemes in South Korea

This article examines the past, present and future of income maintenance schemes in Korea. Historically, income security schemes have been built on the idea of work-centred social insurance supplemented by social assistance. This approach was based on the premise of full employment. While current schemes have achieved institutional completeness and have contributed to alleviating poverty and inequality, they have exhibited limitations in responding to the qualitative shifts of capitalism, leading to welfare blind spots. Various alternatives have been proposed, such as universal basic income (UBI), which aims for equality, and residual Safety Income (SI), which aims for efficiency. The objective of this study is to validate the effects of basic income proposals and SI as alternative income maintenance schemes emerging in Korea. We simulated and compared the poverty alleviation and income redistribution effects of the two alternatives using data from the Survey of Household Finances and Living Conditions (2019~2021). The effects of poverty alleviation and income distribution were determined by analysing the hypothetical changes in absolute and relative poverty rates, as well as the Gini coefficient. The efficiency of benefits was assessed as the ratio of the amount used to reduce the poverty gap out of the total benefit amount. The study found that while SI appeared cost-effective in addressing absolute poverty, UBI was also effective in addressing relative poverty and income inequality.

Studies of tractor maintenance and replacement strategies of Wonji Shoa Factory, Ethiopia

This study mainly focuses on tractor maintenance and replacement strategies to assess the impact of various parameters on the economic life of tractors in order to improve the value of a profitable management choice on selected tractor samples. Considering the preventive replacement policy, the total annual costs were estimated taking into account the repair and depreciation costs. At a 95% level of confidence for each approach, the statistical analysis program “IBM SPSS Statistics 26” was used. An empirical relation based on multiple regression analysis has been generated to predict the economic operational life of a tractor using per-unit repair cost and annual usage (hours). From the analysis, John Dear 333, SAME 130, New Holland 80, and Massy Ferguson 150 are not supposed to be used economically in the field after the fifth, seventh, sixth, and eighth years respectively at Wonji Shoa Sugar Factory due to increasing maintenance cost in present condition.

An In-Depth Study of Vibration Sensors for Condition Monitoring

Heavy machinery allows for the efficient, precise, and safe management of large-scale operations that are beyond the abilities of humans. Heavy machinery breakdowns or failures lead to unexpected downtime, increasing maintenance costs, project delays, and leading to a negative impact on personnel safety. Predictive maintenance is a maintenance strategy that predicts possible breakdowns of equipment using data analysis, pattern recognition, and machine learning. In this paper, vibration-based condition monitoring studies are reviewed with a focus on the devices and methods used for data collection. For measuring vibrations, different accelerometers and their technologies were investigated and evaluated within data collection contexts. The studies collected information from a wide range of sources in the heavy machinery. Throughout our review, we came across some studies using simulations or existing datasets. We concluded in this review that due to the complexity of the situation, we need to use more advanced accelerometers that can measure vibration.

A maintenance strategy for hydraulic systems based on generalized stochastic Petri nets under epistemic uncertainty

A maintenance strategy for hydraulic systems based on generalized stochastic Petri nets under epistemic uncertainty

Comparative Study on the Dynamic Response of Asphalt Pavement Structures: Analysis Using the Classic Kelvin, Maxwell, and Three-Parameter Solid Models

This study addressed the complex problems of selecting a constitutive model to objectively characterize asphalt mixtures and accurately determine their viscoelastic properties, which are influenced by numerous variables. Inaccuracies in model or parameter determination can result in significant discrepancies between the calculated and measured results of the pavement’s structural dynamic response. To address this, the research utilized the physical engineering principles of asphalt pavement structure to perform dynamic modulus tests on three types of high-content rubberized asphalt mixtures (HCRAM) within the surface layer. The research aimed to investigate the influencing factors of the dynamic modulus and establish a comprehensive master curve. This study also critically evaluated the capabilities of three viscoelastic models—the three-parameter solid model, the classical Maxwell model, and the classical Kelvin model—in depicting the dynamic modulus of HCRAM. The findings indicated a negative correlation between the dynamic modulus of the asphalt mixture and temperature, while a positive association exists between the loading frequency and temperature, with the impact of the loading frequency diminishing as the temperature increases. Notably, the three-parameter solid model was identified as the most accurate in describing the viscoelastic properties of the HCRAM. Furthermore, the dynamic response calculations revealed that most indexes in the surface layer’s dynamic response are highest when evaluated using the three-parameter viscoelastic model, underscoring its potential to enhance the pavement performance’s predictive accuracy. This research provides valuable insights into optimizing the material performance and guiding the pavement design and maintenance strategies.

Exploring the Synergy of Artificial Intelligence and Robotics in Industry 4.0 Applications

This article delves into the transformative collaboration between Artificial Intelligence (AI) and robotics within the context of Industry 4.0 applications. Industry 4.0 represents a paradigm shift in manufacturing, characterized by the integration of advanced technologies. The synergy between AI and robotics plays a pivotal role in reshaping industrial processes, leading to increased automation, predictive maintenance strategies, collaborative robotics (cobots), enhanced quality control, and optimized supply chain operations. AI algorithms empower machines to learn, adapt, and make intelligent decisions, fostering adaptability and efficiency in manufacturing. The seamless integration of AI and robotics not only improves operational processes but also introduces novel approaches to human-robot collaboration, quality assurance, and supply chain management. The article also addresses challenges associated with this integration, such as workforce displacement concerns and the need for standardized communication protocols. As the field continues to evolve, navigating these challenges and capitalizing on the ongoing advancements in AI and robotics will be instrumental in unlocking the full potential of their collaborative synergy, ultimately defining the future landscape of Industry 4.0.

School Facilities Maintenance Strategies and Quality Education Delivery in Public Junior Secondary Schools, Lagos State

This study used five assumptions to guide the examination of the techniques for maintaining school infrastructure and providing high-quality education in public junior secondary schools in Lagos State Education District 1. All 42 public junior secondary school principals and teachers in the chosen education district made up the study's population, which used a descriptive and correlational research approach. 216 principals and teachers made up the sample, which was selected using multistage - stratified, simple random, and purposive selection approaches from the sampled 18 schools. A self-constructed questionnaire with two dimensions, one with 20 items on quality education delivery and the other dimension on school facilities maintenance strategies, were used to collect data for the study. For each dimension, the reliability coefficients for this instrument's were found to be 0.77 and 0.79, respectively with a face and content validity being ensured. Person product moment correlation analysis was used as an inferential statistical technique to analyze the data at the 0.05 level of significance. The research findings indicates a noteworthy correlation between preventative maintenance strategy(r = .813; N=216; p< .05), corrective maintenance strategy (r = .786; N=216; p< .05), routine maintenance strategy (r = .765; N=216; p< .05), emergency maintenance strategy (r = .745; N=216; p< .05), remedial maintenance strategy (r = .925; N=216; p< .05) and quality education delivery in Lagos State Education District I. The study thereafter concluded that school facilities maintenance strategies is apparently a major determining factor of quality education delivery in public junior secondary schools in Lagos State Education District I. Thus, the study's recommendations include, among other things, the requirement that maintenance practices and culture be ingrained in everyone and that all parties involved in the education sector bear responsibility for them in order to guarantee the delivery of high-quality education.

Experimental Study on the Identification and Diagnosis of Dynamic Crack Propagation in the Piston Rods of Process Gas Compressors in Underground Gas Storage

Ensuring the reliability of process gas compressors is critical for underground gas storage, as piston rod fractures can lead to serious accidents, such as natural gas leaks or explosions. On-time monitoring and early detection play a vital role in preventing catastrophic consequences, minimising costs, and reducing production losses due to unplanned downtime. This study presents a novel accelerated life-testing method designed to replicate the fracture events of reciprocating compressor piston rods. By accelerating the induced crack initiation and propagation to the final fracture, comprehensive analyses of the fracture results are performed to reveal the piston rod fracture mechanism and the resulting secondary damage to the unit. The research further presents an innovative approach for identifying piston rod crack propagation by means of acoustic emission. Through kinetic analysis and time–frequency domain analysis, the study elucidates two mechanisms responsible for triggering crack signals during the compressor operation: the contact impact between the crosshead pin and the bearing due to the piston rod load reversal, and crack propagation occurring before the maximum tensile load is reached. In addition, the study identifies the piston rod crack expansion signal frequency band and achieves a high-sensitivity identification of crack dynamic growth by extracting signal sub-band features associated with crack propagation. Then, a prediction model of the fatigue crack growth rate was established based on the AE energy release rate, which provides a quantitative assessment of dynamic crack propagation during compression. This method aims to provide a maintenance strategy for piston rod fractures, thereby increasing the operational safety of critical dynamic equipment in underground gas storage.