Failure Frequency Research Articles

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.

Size scaling of large landslides from incomplete inventories

Abstract. Landslide inventories have become cornerstones for estimating the relationship between the frequency and size of slope failures, thus informing appraisals of hillslope stability, erosion, and commensurate hazard. Numerous studies have reported how larger landslides are systematically rarer than smaller ones, drawing on probability distributions fitted to mapped landslide areas or volumes. In these models, much uncertainty concerns the larger landslides (defined here as affecting areas ≥ 0.1 km2) that are rarely sampled and often projected by extrapolating beyond the observed size range in a given study area. Relying instead on size-scaling estimates from other inventories is problematic because landslide detection and mapping, data quality, resolution, sample size, model choice, and fitting method can vary. To overcome these constraints, we use a Bayesian multi-level model with a generalised Pareto likelihood to provide a single, objective, and consistent comparison grounded in extreme value theory. We explore whether and how scaling parameters vary between 37 inventories that, although incomplete, bring together 8627 large landslides. Despite the broad range of mapping protocols and lengths of record, as well as differing topographic, geological, and climatic settings, the posterior power-law exponents remain indistinguishable between most inventories. Likewise, the size statistics fail to separate known earthquakes from rainfall triggers and event-based triggers from multi-temporal catalogues. Instead, our model identifies several inventories with outlier scaling statistics that reflect intentional censoring during mapping. Our results thus caution against a universal or solely mechanistic interpretation of the scaling parameters, at least in the context of large landslides.

Height Control System for Wind Turbines Based on Critical Wind Speed Calculation

The increasing frequency of wind turbine failures due to extreme weather conditions necessitates the implementation of new solutions to enhance their operational reliability. This paper presents an automatic rotor drop system specifically designed for wind turbines equipped with the Onipko rotor. The system aims to protect turbines from damage caused by critical wind speeds, reducing maintenance costs and extending the equipment’s lifespan. The unique design of the Onipko rotor allows it to operate at wind speeds as low as 0.1 m/s. However, its high drag coefficient and lack of aerodynamic optimization make it susceptible to mechanical stress and structural instability under strong gusts, requiring additional protective measures. The paper presents a calculation of the critical wind speed at which protective measures must be initiated. Through mathematical modeling, this study demonstrates the effectiveness of the rotor drop system in ensuring safe operation at wind speeds reaching 23.5 m/s. The optimization of the PI controller parameters provides a rapid response and stability, significantly enhancing the resilience of wind turbines to adverse weather conditions.

Bayesian inference of forest road failure frequency depending on rainfall intensity for every prefecture in Japan

ABSTRACT To make a quantitative prediction on forest road failures in the future under the influence of climate change, forest road failure frequency depending on rainfall intensity needs to be clarified. The forest road failure frequency at a certain rainfall intensity should vary regionally because of the variability in susceptibility. In the current situation of a lack of nationwide detailed forest road failure inventory data and GIS-based forest road data, this study analyzed the relationship between rainfall intensity and forest road failure frequency based on Bayesian inference using data available for all the 47 prefectures in Japan; the annual number of forest road failures, forest road length, analyzed precipitation data and polygon data of forest areas. The predicted forest road failure frequency varied broadly by prefecture; 10−4–10−2 km−1 at a rainfall event of 100 mm/24 h, while 10−2–100 km−1 at that of 400 mm/24 h. The frequency at the same rainfall intensity tended to be lower in conventionally rainy prefectures. The results imply that the number of forest road failures in currently less rainy regions may show a larger increase in the future as intense rainfall events are predicted all over Japan due to climate change.

Evaluation and Long-Term Prediction of Annual Wind Farm Energy Production

A comparison and evaluation of the AEP(Annual Energy Production) of a wind farm were conducted in this study with a feasibility study and using the actual operation data from the S wind farm on Jeju Island from January 2020 to December 2022. The free wind speed data were selected from the data measured from a nacelle anemometer, the correlation equation between wind speed and AEP was obtained, and the annual average wind speed for the past 20 years was predicted using the MCP method. As a result, comparing the AEP from the operation data with that estimated in the feasibility study, we found that the AEP was reduced by approximately 2.40% in 2020 and 12.14% in 2021, and increased by 6.76% in 2022. The wind speeds over the 20-year lifetimes of the wind turbines were obtained, and the AEP that could be generated at the S wind farm indicated that it could be used for operation. In the future, the S wind farm will operate at between 25% and 30% availability for the remaining 17 years of operation. If the availability falls below 25%, there will be a need to check the reasons for the deterioration of wind turbine performance and the frequency of failures.

Phase-based video vibration measurement and fault feature extraction method for compound faults of rolling bearings

Vibration characterization of rotating machinery is crucial for determining rotational and failure frequencies. Traditional contact measurement methods have limitations, while high-speed cameras offer a non-contact alternative for measuring target vibrations, spatial phase-based techniques have recently been widely used in detecting subtle vibrations and show good robustness to imaging noise. In this paper, a vision-based vibration extraction method for rotating machinery is proposed, aiming at extracting minor vibrations of bearings from them and further analyzing their fault frequencies. To address the challenge of separating a single fault frequency from the extracted compound faulty vibration signals, kurtosis is employed to analyze the inverse convolution period of multiple periodic components, and the MOMEDA filter length is optimized using the golden section algorithm. MOMEDA is then used to enhance each periodic pulse separately, and fault frequency is obtained through envelope spectrum demodulation. In the experimental part, a phase-based method is used to extract minor vibration displacements from rotor vibration video, which is subsequently compared with eddy current sensors in both time and frequency domains to verify the accuracy of the proposed method in extracting vibration displacements based on vision. Finally, vibration signals are extracted from the bearing compound fault video, and the single fault frequency characteristics of the bearing are successfully separated using the adaptive MOMEDA method, which provides an efficient and reliable method in the field of rotating machinery fault diagnosis.

Failure Pattern and Reliability Analysis of Mobile Biomass Gasifier Engine (Prototype 3) using Weibull Calculation as a Basis for Updating the Preventive Maintenance Activity Schedule

Gasification is a technology that optimistically utilizes biomass to produce syngas (consisting of H2, CO, CO2, CH4) that can be used as an energy source while reducing excess CO2 emissions. However, if there are frequent failures due to unplanned maintenance activities, the engine performance will not be optimal. Therefore, an up-to-date reliability study is needed as a basis for updating preventive maintenance activities to maintain the quality of the engine so that it continues to operate properly and is durable. This research aims to analyze the reliability, unreliability, and availability of the Mobile Biomass Gasifier (Prototype3), as well as study the failure patterns measured using the shape parameter (?) with the Weibull distribution. The focus of this research is on the Suction blower which based on Pareto results is the equipment with the highest failure frequency. The results show that the average reliability is smaller than the failure rate. In addition, the value of the shape parameter (?) > 1, which means that the damage rate increases as the component ages. The implication of this study shows the need to update the preventive maintenance (PM) schedule by considering the addition of new PM activities that are in accordance with the duration of the life of the components that cause machine damage.

Self-control failures, as judged by themselves

AbstractThe existence of self-control failures is often used to legitimize public policy interventions. The argument is that reducing self-control failures can make people better off, as judged by themselves. However, there is only scarce evidence on the frequency and welfare costs of self-control failures. This paper presents a survey method that allows us to measure self-control failures in everyday life and to identify their welfare costs in terms of associations with experienced subjective well-being. We present novel survey evidence using this method and discuss its implications for behavioural welfare economics and behavioural public policy.

Fault diagnosis method of switch machine based on adaptive neural fuzzy network

Aiming at the problem of a large number of switch machines, harsh working environment and high frequency of equipment failures caused by many factors, in order to accurately diagnose railway switch machine faults, a switch machine fault diagnosis method based on adaptive neural fuzzy network (ANFIS) optimized by dynamic weight particle swarm (DPSO) algorithm is proposed by analyzing the vibration signals generated during the operation of the switch machine. Firstly, the working vibration signal is decomposed into several intrinsic mode functions (IMFs) and selected by using the ensemble empirical mode decomposition (EEMD) algorithm; then, the feature values of IMFs are extracted by using the improved time domain multiscale scatter extraction (ITMDE) algorithm, and then input into the optimized ANFIS model to learn and realize fault diagnosis; finally, the method is compared and analyzed with various diagnostic model algorithms and learning algorithms. The experimental results show that the proposed method can effectively diagnose switch machine faults, which has certain reference significance for intelligent diagnosis of switch machine faults and related research in the future.

Poly-ether-ether-ketone (PEEK) versus dead-soft coaxial bonded retainers: a randomized clinical trial. Part 1. Stability, retainer failure, and participant satisfaction.

Poly-ether-ether-ketone (PEEK) was introduced in dentistry as an alternative to metal alloys. To assess the effectiveness of PEEK-fixed retainers in preserving the stability of mandibular anterior and participant satisfaction as compared to the Dead-soft coaxial fixed retainer (DSC). A single-centre, two-arm parallel groups randomized clinical trial. The patients treated with pre-adjusted orthodontic appliances who have a Little's Irregularity Index (LII) ≤ 0.5mm have been enrolled in the trial. PEEK retainers were prepared to round 0.8mm wire by computer-aided design and manufacturing, and the DSC wire was carefully adapted to the lingual surface of the lower anterior teeth. The primary outcome was the stability of lower anterior teeth as assessed by LII, while the secondary outcomes were changes in occlusal parameters, retainer failure, and patient satisfaction. The data were collected at the debonding stage (T0), 1 month (T1), 3 months (T3), and 6 months (T6) after starting the trial, except for patient's satisfaction, which was recorded using an electronic form at T1 and T6. Single blinding of participants. A total of 46 participants with an age range of 12-28 years old were randomly allocated to the two groups (n = 23 in each). Only one participant dropped out; therefore, 45 participants were analysed. The DSC group showed a significant increase in LII at T3. Both retainer groups had comparable occlusal measurements, failure frequency, and survival time, with no significant difference. The patients in the DSC group reported a statistically significant perception of change in the position of their teeth compared to those in the PEEK group. No harmful effects have been reported. Limited follow-up duration and the inability to blind the operator due to the nature of the intervention. After 6-month retention, the PEEK retainer was equally effective to DSC retainers in maintaining the teeth alignment, with no significant differences regarding the failure frequency, survival rate, and general patient satisfaction. https://register.clinicaltrials.gov. (NCT05557136).

COFUN: A new tool for evaluating containment failure frequency and uncertainty

The feature of the COntainment Failure probability and Uncertainty aNalysis program (COFUN) is the quantification of the containment state. The probability factors used in probabilistic safety assessment (PSA) and the resulting outputs contain probability bands, so it is necessary to present the uncertainty bands as results. The uncertainty analysis process of the level 2 PSA is that a level 1 PSA uncertainty propagates to a level 2 PSA uncertainty. The basic event data including probability distribution were used for the level 1 PSA uncertainty analysis and probability distribution and priority of the fractional branch point were used for uncertainty analysis in a level 2 PSA. Once the input is ready, uncertainty analysis is performed via Monte-Carlo sampling. Finally, the uncertainty analysis can be performed for source term category (STC) and large early release frequency (LERF). One of the outstanding characteristics of the COFUN is to perform level 2 PSA for multi-unit accidents. The one of formats of multi-unit level 2 PSA is the combination of STCs but the combinations could be too many to acquire insight by STC combination itself. The COFUN provides a user-defined combination that makes STC combinations classified and enables the derivation of multi-unit accident LERF result.

PERANCANGAN SISTEM PERAWATAN PULVERIZER MODEL MVM31F MENGGUNAKAN METODE FAILURE MODE AND EFFECT ANALYSIS (FMEA) DI PT. XYZ

The continuous increase in population growth leads to a rising demand for electrical energy. PT. XYZ as a supplier of electrical energy, relies heavily on the reliability of its components to meet this demand. The frequency of failures in pulverizer machines is one of the factors that reduce production capacity. This study uses the FMEA method combined with LTA to determine the failure consequences caused by failure modes, resulting in appropriate maintenance actions for critical components. From the FMEA analysis, 10 failure modes were identified along with their causes and effects if failures occur. Based on the RPN value calculations, a critical value of 69.2 was obtained. Components with RPN values above the critical value that need prioritized maintenance are the classifier and grinding roller. In the maintenance planning stage, 7 failure modes are placed under time-directed (TD) maintenance, 2 failure modes under condition-directed (CD) maintenance, and 1 failure mode under finding failure (FF) maintenance.

Fault Feature Extraction of Rolling Bearing Based on Maximum Second-Order Cyclostationarity Blind Deconvolution and CEEMD-Teager

The operation of rolling bearings is directly related to the reliability of the whole rotating machinery. If the rolling bearing failure cannot be diagnosed and repaired in time, it will probably lead to equipment shutdown. The feature extraction process is an important part of bearing fault diagnosis. Some existing feature extraction methods are sensitive to noise and interference, and cannot fully explore and characterize useful information in nonlinear and non-stationary signals in complex scenes, and sometimes even lose important information contained in the data, resulting in low diagnostic accuracy. Therefore, a new method combining Maximum Second-order Cyclostationarity Blind Deconvolution (CYCBD) and Complementary Ensemble Empirical Mode Decomposition (CEEMD) was proposed for feature extraction of rolling bearing faults. Firstly, the cyclic frequency is set according to the failure frequency, and the original signal is filtered by CYCBD. Then, the filtered signal is decomposed into multiple Intrinsic Mode Function (IMF) by CEEMD, and the effective IMF components are selected by kurtosis criterion for reconstruction. Finally, the Teager energy operator is used to enhance the transient impact of the reconstructed signals. The results of simulation and comparison experiments show that the proposed method can extract bearing characteristics in different fault states more effectively, and the accuracy of network model diagnosis is improved compared with the traditional methods.

Developing a novel anticipated inherent risk assessment approach applicable in early stages of process design

Inherently Safer Design (ISD), as one of the most fundamental approaches to process safety, focuses on the innate design features. Numerous tools have been developed to assess ISD and risk-based indices are one of the most elevated of them. Existing indices are limited in modelling consequences or are challenging to adapt in terms of probability. This study introduces the Anticipated Inherent Risk Index (AIRI), a robust tool for rapid risk assessment of the worst anticipated scenarios. It incorporates Furthest Effect Distance of the release from the largest connection, by software, and Representative Failure frequency, which is a relative dataset calculated from renown databases. This presents a relative risk map upon major consequences and their likelihood. AIRI applied to a PDH Fractionation Subunits shows that Heat Pump Compressor Suction Drum (AIRI≈3399) in PP Splitter Section, followed by Propylene Trim Condenser (AIRI≈1931) and PP Splitter Reboiler/Condenser (AIRI≈1798) are the equipment with the highest risk. AIRI is a competent tool for early high-level analysis of equipment risk, which gives useful indications on design and how to improve it. This tool also has the potential to be equipped with vaster frequency datasets and effects analysis and to be incorporated into other process safety studies.

The Effect of Opioids and Benzodiazepines on Exacerbation Rate and Overall Survival in Patients with Chronic Obstructive Pulmonary Disease on Long-Term Non-Invasive Ventilation.

Background: There is a growing concern that opioids and benzodiazepines can depress the respiratory drive and could contribute to worsening respiratory failure and higher exacerbation frequency in COPD. However, the relationship between the exacerbation rate and medication taken is poorly understood in patients with chronic respiratory failure due to COPD. Methods: As part of a service evaluation project, we analysed 339 patients with COPD who were established on long-term non-invasive ventilation (LT-NIV) at our tertiary centre. We investigated the relationship between benzodiazepine and opioid prescription and clinical outcomes as well as their impact on the exacerbation rate and overall survival following setup. Results: Before LT-NIV setup, 40 patients took benzodiazepines and 99 patients took opioids. Neither benzodiazepine nor opioid use was associated with changes in daytime blood gases, overnight hypoxia or annual exacerbations before NIV setup, but patients taking opioids were more breathless as assessed by modified Medical Research Council scores (3.91 ± 0.38 vs. 3.65 ± 0.73, p < 0.01). Long-term NIV significantly reduced the number of yearly exacerbations (from 3.0/2.0-5.0/ to 2.8/0.71-4.57/, p < 0.01) in the whole cohort, but the effect was limited in those who took benzodiazepines (from 3.0/2.0-7.0/ to 3.5/1.2-5.5/) or opioids (3.0/2.0-6.0/ to 3.0/0.8-5.5/). Benzodiazepine use was associated with reduced exacerbation-free survival and overall survival (both p < 0.05). However, after adjustment with relevant covariates, the relationship with exacerbation-free survival became insignificant (p = 0.12). Opioids were not associated with adverse outcomes. Conclusions: Benzodiazepines and opiates are commonly taken in this cohort. Whilst they do not seem to contribute to impaired gas exchange pre-setup, they, especially benzodiazepines, may limit the benefits of LT-NIV.

A study on comparisons of additive regression frailty models to counter heterogeneity: Bayesian strategies and case study

Historically, the primary goal of conventional survival study methods has been to reduce the frequency of failures over time. If the associated observed and unobserved variables are not known when studying such events, this can have detrimental effects. Frailty models offer a tempting solution for investigating the impact of unknown variables in such a case. In this article, we assume that frailty affects the hazard rate. We find that the weighted Lindley frailty models, which use general versions of the Weibull and log-logistic type II distributions as the baseline distributions, are a reliable method for ensuring the influence of endogenous variability. The parameters involved are estimated according to different loss functions using the Bayesian structure as the basis of Markov Chain Monte Carlo. Bayesian evaluation strategies are then implemented to evaluate the models. The results are demonstrated on known data of kidney infections. It is shown that the novel models outperform those based on the inverse Gaussian and gamma frailty distributions.

Implementasi Metode Reliability Centered Maintenance untuk Menunjang Ketersediaan Suku Cadang Unit Prime Mover Truck Tipe 550 Horse Power

One of mining materials transport units is a 550 horse power prime mover truck. The prime mover truck functions as the main power source of trailer unit. This research was motivated by unexpected failures of 550 horsepower prime mover truck which operating on extreme mining environments, and unreachable component lifetime also delays of part supply, which contributed to increased downtime. The research purposes are to improve and develop the best maintenance system by identifying critical components, determining reliability values, determine maintenance intervals, and ensuring spare parts availability. The research was conducted using the Reliability Centered Maintenance (RCM) method, with independent variables are failure frequency data and failure duration data. The results identified five critical components, air piping and hose, fast fuel filler, radiator components, radiator hose, and wheel stud and nut. Maintenance intervals were determined when the reliability value approached 0.6, with the air piping and hose component reaching 1750 hours of operation or every 97 days, the fast fuel filler component reaching 1,250 hours or every 69 days, the radiator and reservoir tank component reaching 2250 hours or every 125 days, the radiator hose and line component reaching 2750 hours or every 152 days, and the wheel stud and nut component of 3000 hours or every 166 days.

Effect of Implant Maintenance on Incidence of Peri-implantitis and Early Implant Failure: Retrospective Cohort Study.

Maintaining dental implants and managing peri-implant tissues has become integral to dental practice. Owing to the larger number of implants placed, the number of peri-implant lesions is increasing. Periodontal implant maintenance therapy (PIMT) provides a conservative long-term treatment modality to monitor and maintain implants. This study aims to investigate periodontal maintenance effects on peri-implant tissue health to add to existing evidence. A retrospective analysis included implant patients with 1-7 years of follow-up, assessing peri-implantitis and early implant failure as outcome variables. The frequency of PIMT, implant characteristics, and demographics served as predictors, and associations between peri-implantitis, implant failure, and PIMT frequency were analyzed using Fisher exact tests with the significance level set at .05. Data were collected on 501 implants from 185 patients. Twenty-nine (6%) demonstrated peri-implantitis, whereas 22 (5%) experienced early implant failure. A significant association between PIMT and peri-implantitis was observed (p-value = .0169) with the rate of peri-implantitis at 9% (n = 20) for patients without PIMT and only 4% (n = 10) for those with PIMT. Whereas PIMT was not significantly associated with early implant failure (p-value = .4372), peri-implantitis was: 25% of implants with peri-implantitis experienced early failure compared with 4% without (p-value = .0062). Considering the limitations of the present study, the absence of PIMT was found to be associated with peri-implantitis, which was itself associated with early implant failure. Implant maintenance at 6-month intervals is important in maintaining peri-implant health.

Omega-3 fatty acids-critical for the marine food web and for seabird productivity

Abstract Seabird reproductive success in northern latitudes is often linked with prey abundance, availability, size, or species. Causes are often broadly explained as changes in ocean currents/temperatures, or diets. Few studies trace diets down the food web to primary producers, thus missing what I propose as an underlying cause of seabird colony failure, dietary abundance of essential omega-3 polyunsaturated fatty acids (PUFA). PUFA differ from other nutrients because they are, in part, ligands—critical for reproduction and other physiological processes for the entire marine food web, not just for seabirds. Diatoms are one of the few life forms that can produce PUFA de novo, and a lack of PUFA in zooplankton and fish reduces their abundance and productivity, with consequences up the food web. Since the mid-1980s, historical prey of breeding seabirds has decreased, and frequency of colony failures has increased. In years of failure, prey often had less fat than historically preferred species. Likewise, proportions of alternate species’ PUFA, from published papers, show lower values than in historical prey. Production of PUFA by diatoms is depressed in warmer and more acidic environments, and I hypothesize that these warmer and more acidic seas have affected production of PUFA over the past 3–4 decades. Assuming this is true, I propose that these lower amounts of PUFA have negatively impacted the breeding success of all members of the marine food web, not just seabirds.

Efficacy of first-line ART regimens based on tenofovir in HIV-infected patients with pre-existing A62V mutation in reverse transcriptase.

The amino acid substitution A62V in reverse transcriptase was identified as a mutation correlated with virologic failure in patients on first-line therapy including tenofovir (TDF) and tenofovir alafenamide (TAF). A62V is a typically polymorphic mutation in HIV-1 sub-subtype A6, which is the most widespread virus variant in Russia. The European EuResist (EIDB) database was queried to form two equivalent groups of patients: group 1 ‒ patients with A62V at baseline treated with TDF or TAF on the first-line therapy, group 2 ‒ patients without A62V at baseline treated with TDF or TAF on the first-line therapy. Each group included 23 patients. There was no statistical difference between the two groups in virologic efficacy in 4, 12, and 24 weeks after the start of antiretroviral therapy (ART) and in the frequency of virologic failures. This study has some limitations, and the exact role of A62V in the efficacy of the first-line ART based on tenofovir deserves further investigation.