Station Downtime Research Articles

Hydrogen station prognostics and health monitoring model

Hydrogen fuel has shown promise as a clean, alternative fuel aiding in the reduction of fossil fuel dependence within the transportation sector. However, hydrogen refueling stations and infrastructure remains a barrier and are a prerequisite for consumer adoption of low-cost and low-emission fuel cell electric vehicles (FCEVs). The costs for FCEV fueling include both station capital costs and operation and maintenance (O&M) costs. Contributing to these O&M costs, unscheduled maintenance is presently more costly and more frequent than for similar gasoline fueling infrastructure and is asserted to be a limiting factor in achieving FCEV customer acceptance and cost parity. Unscheduled maintenance leads to longer station downtime, therefore, causing an increase in missed fueling opportunities, which forces customers to seek refueling at other operable stations that may be significantly farther away. This research proposes a framework for a hydrogen station prognostics health monitoring (H2S PHM) model that can minimize unexpected downtime by predicting the remaining useful life for primary hydrogen station components within the major station subsystems. The H2S PHM model is a data-driven statistical model, based on O&M data collected from 34 retail hydrogen stations located in the U.S. The primary subcomponents studied are the dispenser, compressor, and chiller. The remaining useful life calculations are used to decide whether or not maintenance should be completed based on the prediction and expected future station use. This paper presents the background, method, and results for the H2S PHM model as for a means for improving station availability and customer confidence in FCEVs and hydrogen infrastructure.

Photovoltaic-battery powered bike share stations are not necessarily energy self-sufficient

Many cities use solar photovoltaic (PV) panels to power off-grid bike share stations to provide sustainable transportation. When a station's energy demand for operating the station’s kiosk and docks exceeds the PV panel's supply, the battery can be depleted, requiring manual battery replacement to avoid service disruption. However, existing research on siting, modeling, or assessing the environmental impacts of bike share stations has not accounted for spatially variable solar PV potential and energy usage. This study addresses this gap to analyze the performances of PV-battery systems for bike share stations and evaluates strategies to improve their energy independence, sustainability, and system reliability. Using Chicago’s Divvy bike share system as a case study, we simulated the battery charging, discharging, and replacements, and measured their performances with three metrics: energy self-sufficiency level, energy payback time (EPBT), and station downtime. We found that PV energy alone is insufficient to meet all energy needs of bike share stations, with the average energy self-sufficiency levels being 56%. Additionally, relying solely on battery replacement during rebalancing operations was insufficient to maintain stations' operational status. On average, bike share operators need to manually replace a station’s batteries 72 times a year. Furthermore, the current PV panels for most stations are sized too small. Increasing panel sizes could reduce most stations' downtime and increase their self-sufficiency without significantly affecting EPBT. The study highlights the need for bike share system modeling and operation planning to account for battery replacement needs and demonstrates the importance to evaluate smaller yet large-scale street-level PV applications. The modeling framework presented in this study can also be extended to examine other smaller PV-battery systems similar to bike stations.

Booster Bearing Failure Reduction Through a Novel Thermodynamic Analysis

The gas booster station of a steel works has experienced excessive bearing failures since commissioning over two decades ago. This station was designed with redundancy, allowing for automatic switch-over between two gas booster fans. Bearing failures were observed, on average once every 15.7 days, with instances where both fans experienced simultaneous downtime. Booster failures resulted in regular station downtime, preventing Coke Oven Gas (COG) transport to an end user. This flammable by-product is used as a heat source and all unutilized volumes are flared, resulting in energy wastages. Furthermore, the absence of COG increases Natural Gas (NG) usage, procured at a cost. Traditional root cause analysis techniques failed to identify the cause of these excessive bearing failures. However, multiple in-depth data analysis studies resulted in a thermodynamic investigation, exposing liquid and solid particles within the COG to be responsible for the failures. This allowed for the design of an in-line particle collector, eliminating excessive failures. Following the particle collector installation, only two strategic bearing changes took place over the next 41 weeks, with reduced bearing vibration levels compared to before. The station experienced no failure downtime during this period, resulting in reduced COG flaring and thus improved energy utilization.

The Mackenzie Mountains EarthScope Project: Studying Active Deformation in the Northern North American Cordillera from Margin to Craton

Abstract The Mackenzie Mountains EarthScope Project—a collaboration between Colorado State University, the University of Alaska, Michigan State University, and Yukon College—deployed a roughly linear, 40-station broadband seismographic network. This network crossed the actively deforming Northern Canadian Cordillera and the Mackenzie Mountains in Yukon, Canada; it also extended into the Canadian Shield in Northwest Territories, Canada. The array was deployed between July 2016 and August 2018 (with four pilot stations installed in July 2015 and three extended stations operating through August 2019) coinciding with and complementing the deployment of the EarthScope Transportable Array to Alaska and western Canada. In this article, we present an overview of project scientific objectives, station configurations, and site conditions; discuss environmental challenges, including those that resulted in station downtime (e.g., spring flooding and encounters with bears); and suggest potential solutions to such subarctic challenges for the benefit of future deployments in comparable regions. We also include an initial characterization of seasonal and geographic variations in ambient seismic noise for the northwestern Canadian Cordillera.

Adapted tube cleaning practices to reduce particulate contamination at hydrogen fueling stations

The higher rate of component failure and downtime during initial operation in hydrogen stations is not well understood. The National Renewable Energy Laboratory (NREL) has been collecting failed components from retail and research hydrogen fueling stations in California and Colorado and analyzing them using an optical zoom and scanning electron microscope. The results show stainless steel metal particulate contamination. While it is difficult to definitively know the origin of the contaminants, a possible source of the metal particulates is improper tube cleaning practices. To understand the impact of different cleaning procedures, NREL performed an experiment to quantify the particulates introduced from newly cut tubes. The process of tube cutting, threading and beveling, which is performed most often during station fabrication, is shown to introduce metal contaminants and thus is an area that could benefit from improved cleaning practices. This paper shows how these particulates can be reduced, which could prevent station downtime and costly repair. These results are from the initial phase of a project in which NREL intends to further investigate the sources of particulate contamination in hydrogen stations.

Lessons learned from the installation and operation of Northern California's first 70-MPa hydrogen fueling station

A hydrogen dispensing facility capable of providing rapid 70-MPa vehicle fills became operational in May 2011 as the first such hydrogen dispensing facility in Northern California. The facility is operated by the University of California – Berkeley in support of fuel cell vehicle research with automakers, regional and state agencies, and industrial groups. In addition to storing and dispensing high-pressure hydrogen fuel, the station also incorporates a number of key advances in hydrogen refueling system capabilities, including novel fuel pre-cooling, fuel storage, and system safety systems. Key lessons learned from the construction and initial operation of the station include: 1) extensive initial planning is essential for smooth project development; 2) permitting is a key step and early engagement with local officials is critical; 3) extensive safety reviews may be required; 4) site work should be conducted with careful planning and execution; 5) methodical system commissioning is a key step in the project development process; 6) careful station maintenance and operational planning is critical for minimization of station downtime; and 7) station upkeep and utility expenses can be considerable.

MANAGING BOTTLENECKS IN MANUAL AUTOMOBILE ASSEMBLY SYSTEMS USING DISCRETE EVENT SIMULATION

Batch model lines are quite handy when the demand for each product is moderate. However, they are characterised by high work-in-progress inventories, lost production time when changing over models, and reduced flexibility when it comes to altering production rates as product demand changes. On the other hand, mixed model lines can offer reduced work-in-progress inventory and increased flexibility. The object of this paper is to illustrate that a manual automobile assembling system can be optimised through managing bottlenecks by ensuring high workstation utilisation, reducing queue lengths before stations and reducing station downtime. A case study from the automobile industry is used for data collection. A model is developed through the use of simulation software. The model is then verified and validated before a detailed bottleneck analysis is conducted. An operational strategy is then proposed for optimal bottleneck management. Although the paper focuses on improving automobile assembly systems in batch mode, the methodology can also be applied in single model manual and automated production lines.

Transient Analysis of Downtimes and Bottleneck Dynamics in Serial Manufacturing Systems

In manufacturing industry, downtimes have been considered as major impact factors of production performance. However, the real impacts of downtime events and relationships between downtimes and system performance and bottlenecks are not as trivial as it appears. To improve the system performance in real-time and to properly allocate limited resources/efforts to different stations, it is necessary to quantify the impact of each station downtime event on the production throughput of the whole transfer line. A complete characterization of the impact requires a careful investigation of the transients of the line dynamics disturbed by the downtime event. We study in this paper the impact of downtime events on the performance of inhomogeneous serial transfer lines. Our mathematical analysis suggests that the impact of any isolated downtime event is only apparent in the relatively long run when the duration exceeds a certain threshold called opportunity window. We also study the bottleneck phenomenon and its relationship with downtimes and opportunity window. The results are applicable to real-time production control, opportunistic maintenance scheduling, personnel staffing, and downtime cost estimation.

Capacity improvement of an unreliable production line—an analytical approach

This paper addresses the problem of capacity estimation and improvement of a multi-stage, multi-product production line where workstations are subject to random failure and repair. The production line can process a variety of products in a batch production environment. Products are processed according to a predefined sequence. A linear programming model is used and modified by taking into account the random behaviour of unreliable stations. Station's downtime is modelled as a fictive product added to the production sequence at appropriate positions. A general procedure for the insertion of fictive products is presented. The procedure considers the states up and down that a station may experience while processing the product mix. It consists of two main steps. Firstly, enumerate the station's states and insert fictive products where appropriate. Secondly, find the best buffer size that minimizes the cycle time. The proposed approach considers more parameters than the Markovian models and the approximation methods where multi-product production lines longer than 2-station 1-buffer can be studied. Numerical examples are presented to show all the steps involved to compute the expected cycle time. Buffer contribution to minimize the cycle time of the production line is also addressed. Simulation is used to validate the results obtained.

The impact of restricting the flow of inventory in serial production systems

This research uses simulation technique to compare the output performance of pull production systems with different inter-station storage capacities (ISSC) under various levels of total inventory (INV) in the system, station coefficient of variation (CV), and station down-time (DT). There are two primary findings from this study. First, it shows pull systems with larger ISSC yield a significantly higher output at all levels of INV in the system, station CV, and station DT. Second, it shows that lines using a strict kanban-based pull system can increase output while decreasing WIP inventory simply by allowing the inventory to flow freely between stations. These are major findings considering that the modifications necessary in the system to achieve these results are fairly uncomplicated and should require minimal investment.

A comparative study of line design approaches for serial production systems

Over the past decade two approaches, just‐in‐time (JIT) and theory of constraints (TOC), for designing and operating production lines have developed, each claiming to be the “correct” way. In addition there are still those who maintain that line balanced (whenever possible) is the optimal method. This study uses simulation to compare each of these approaches for designing and operating production lines under various levels of processing time variability, station downtime, and total system inventory. Not surprisingly, the JIT approach appears to work best when system variability is low. The TOC approach works best when system variability is high. This shows that lines designed using TOC principles perform significantly better than JIT lines when inventory is low, and JIT lines perform significantly better than TOC lines as inventory is added to the system. The traditionally balanced line did not perform best under any of the conditions used in this study.