Risk-based threshold on intensity function of repairable systems: A case study on aero engines

Maintenance, repair and overhaul (MRO) facilities deal with situations where repairable systems and its components are required to be designated as high failure rate components (HFRCs). The shortlisted HFRCs are then selected for reliability improvement. The procedure of short listing components as HFRCs is commonly based on experts’ field experience or number of failures. In case of organizations dealing with complex and critical repairable systems like military aviation (MA) and nuclear industries, the subjectivity in the short listing of HFRCs can lead to prolonged unavailability of equipment and may incur financial loss. Thus, a scientific methodology is required to be developed for HFRC designation. The paper develops a methodology for HFRC designation through risk-based threshold on intensity function by considering combat aircraft engines as a case. To develop the threshold methodology, the paper uses generalized renewal process (GRP) for multiple repairable systems (MRS) considering both corrective and preventive maintenance as imperfect. The proposed methodology is duly validated with the help of field failure data of two variants of the same aero engine of a particular combat aircraft. The developed methodology in this paper is highly inspired by the problems faced by the various industries while operating the repairable systems and can be extended for systems which undergo periodic maintenance, repair and overhaul.

Model Selection Methods for Reliability Assessment Based on Interval-Censored Field Failure Samples

Incomplete field failure data from automated production are often applied for evaluating the system reliability. But the evaluation could be impacted by the uncertainty of the product’s lifetime distribution, which is usually predetermined but may be misspecified. In this paper, we assume that the system lifetime distribution follows a location-scale family with several candidates instead of a certain distribution. Two model selection procedures are proposed to assign the most likely candidate distribution from a pool of the location-scale distributions based on interval-censored field failure samples. The maximum likelihood estimates (MLE) of parameters of the candidate distribution are estimated by using the Newton–Raphson method and the MLE of a quartile is assigned as the reliability measure for assessing the reliability of systems. To illustrate the applications of the proposed model selection procedures, an example of high-speed motor with interval-censored field failure data is given. Monte Carlo simulations are carried out to evaluate the performance of the proposed model selection procedures. Simulation results show that the proposed methods are efficient for model identification and can provide reliable reliability assessment.

Bifenthrin Baseline Susceptibility and Evaluation of Simulated Aerial Applications in Striacosta albicosta (Lepidoptera: Noctuidae)

Striacosta albicosta (Smith) is a maize pest that has recently expanded its geographical range into the eastern United States and southeastern Canada. Aerial application of pyrethroids, such as bifenthrin, has been a major practice adopted to manage this pest. Reports of field failure of pyrethroids have increased since 2013. Striacosta albicosta populations were collected in 2016 and 2017 from maize fields in Nebraska, Kansas, and Canada and screened with bifenthrin active ingredient in larval contact dose-response bioassays. Resistance ratios estimated were generally low in 2016 (1.04- to 1.32-fold) with the highest LC50 in North Platte, NE (66.10 ng/cm2) and lowest in Scottsbluff, NE (50.10 ng/cm2). In 2017, O’Neill, NE showed the highest LC50 (100.66 ng/cm2) and Delhi, Canada exhibited the lowest (6.33 ng/cm2), resulting in a resistance ratio variation of 6.02- to 15.90-fold. Implications of bifenthrin resistance levels were further investigated by aerial application simulations. Experiments were conducted with a spray chamber where representative S. albicosta populations were exposed to labeled rates of a commercial bifenthrin formulation. Experiments resulted in 100% mortality for all populations, instars, insecticide rates, and carrier volumes, suggesting that levels of resistance estimated for bifenthrin active ingredient did not seem to impact the efficacy of the correspondent commercial product under controlled conditions. Results obtained from this research indicate that control failures reported in Nebraska could be associated with factors other than insecticide resistance, such as issues with the application technique, environmental conditions during and/or after application, or the insect’s natural behavior. Data generated will assist future S. albicosta resistance management programs.

System Reliability Growth Analysis during Warranty

This paper presents system reliability growth analysis using actual field failure data. The primary objective of the system reliability growth is to improve the achievement of system reliability performance during system reliability demonstration, in order to achieve the predicted or contractually required system reliability commitment. An effective reliability growth model can be utilized to predict when the reliability target can be achieved based on previous reliability performance. In this paper, the system reliability growth analysis is illustrated using the Duane and AMSAA reliability growth models to determine applicability and aid in choice determination. The Duane model is a better choice for failure terminated reliability growth while AMSAA is a better choice for time terminated reliability growth. Comparisons of the Duane versus AMSAA model are carried out by conducting the statistical analysis on the observed field failures.

Integrated reliability and maintainability analysis of Computerized Numerical Control Turning Center considering the effects of human and organizational factors

Purpose Reliability, maintainability and availability of modern complex engineered systems are significantly affected by four basic systems or elements: hardware, software, organizational and human. Computerized Numerical Control Turning Center (CNCTC) is one of the complex machine tools used in manufacturing industries. Several research studies have shown that the reliability and maintainability is greatly influenced by human and organizational factors (HOFs). The purpose of this paper is to identify critical HOFs and their effects on the reliability and maintainability of the CNCTC. Design/methodology/approach In this paper, 12 human performance influencing factors (PIFs) and 10 organizational factors (OFs) which affect the reliability and maintainability of the CNCTC are identified and prioritized according to their criticality. The opinions of experts in the fields are used for prioritizing, whereas the field failure and repair data are used for reliability and maintainability modeling. Findings Experience, training, and behavior are the three most critical human PIFs, and safety culture, problem solving resources, corrective action program and training program are the four most critical OFs which significantly affect the reliability and maintainability of the CNCTC. The reliability and maintainability analysis reveals that the Weibull is the best-fit distribution for time-between-failure data, whereas log-normal is the best-fit distribution for Time-To-Repair data. The failure rate of the CNCTC is nearly constant. Nearly 66 percent of the total failures and repairs are typically due to the hardware system. The percentage of failures and repairs influenced by HOFs is nearly only 16 percent; however, the failure and repair impact of HOFs is significant. The HOFs can increase the mean-time-to-repair and mean-time-between-failure of the CNCTC by nearly 65 and 33 percent, respectively. Originality/value The paper uses the field failure data and expert opinions for the analysis. The critical sub-systems of the CNCTC are identified using the judgment of the experts, and the trend of the results is verified with published results.