Reliability of machine maintenance in The Gambia?
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The reliability of machine maintenance in The Gambia has been studied in several papers. One study focused on the maintenance cost of agricultural machines, specifically a rice combine harvester, and found that the use of a preventive maintenance (PM) strategy did not produce desired results. As a result, a new strategy called condition-based maintenance (CBM) was proposed, which showed improved reliability indicators . Another study analyzed the reliability of a reach stacker and found that as reliability increased, repair and maintenance costs and time decreased . A third study predicted the repair and maintenance cost of farm tractors in The Gambia and found that it increased with an increase in working hours . Finally, a study investigated the coordinated optimization problem of production and maintenance, aiming to minimize total maintenance cost by determining an appropriate PM plan .
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2 Citations | The text information provided does not contain any specific information about the reliability of machine maintenance in The Gambia. |
| The paper does not provide any information about the reliability of machine maintenance in The Gambia. | |
Open access•Journal Article | The paper does not provide information about the reliability of machine maintenance in The Gambia. |
| The reliability of a reach stacker in relation to repair and maintenance cost was analyzed at the Gambia sea port. | |
Open access•Journal Article 2 Citations | The paper assesses the impact of preventive maintenance strategy on reliability indicators of a rice combine harvester in The Gambia. |
Related Questions
How does the implementation of a preventive maintenance plan affect the availability of machines in a processing plant?10 answersThe implementation of a preventive maintenance (PM) plan significantly influences the availability of machines in a processing plant by ensuring that equipment operates at optimal efficiency and reliability, thereby reducing unexpected downtime. Studies have shown that planned preventive maintenance (PPM) and preventive maintenance strategies like Reliability-centered Maintenance (RCM) lead to an improvement in the overall equipment effectiveness (OEE) and availability of production plants. For instance, after implementing PPM, a batching plant machine showed an improvement in OEE, indicating enhanced equipment availability, performance efficiency, and quality product rate. Similarly, the implementation of RCM in a fertilizer company in Indonesia resulted in increased availability of production plants, although it was noted that the effectiveness of RCM varied across different plants, suggesting a need for tailored evaluations.
Moreover, preventive maintenance schedules, when optimized, can maximize system availability while minimizing costs, as demonstrated in a study where a multiobjective model was formulated to achieve these goals, with particle swarm optimization (PSO) proving to be an effective technique. The integration of preventive maintenance into production planning, using predictive algorithms and optimization techniques, further supports the availability of machines by identifying opportunistic periods for maintenance that have less impact on production.
However, challenges in implementing preventive maintenance, such as human errors in reporting and recording maintenance activities, can hinder its effectiveness. The development of applications like the Reminder & Control System Management (RCSM) aims to mitigate these issues by ensuring that preventive maintenance activities are scheduled, recorded, and validated properly. Additionally, preventive maintenance planning, when applied to specific machinery with frequent failures, can significantly reduce non-production costs and improve operational availability through the standardization of intervention procedures.
In summary, the implementation of a preventive maintenance plan positively affects the availability of machines in a processing plant by reducing breakdowns, optimizing maintenance schedules, and ensuring that maintenance activities are conducted efficiently and effectively.
How effective are machine learning algorithms in predicting maintenance needs for mechanical engineering systems?5 answersMachine learning algorithms have shown promise in predicting maintenance needs for mechanical engineering systems. These algorithms utilize machine data and anomaly detection techniques to forecast maintenance requirements. They have been effective in fault diagnosis, time-series analysis, and anomaly detection for predictive maintenance. Deep learning methods, such as Autoencoders (AE), Long Short-Term Memory (LSTM), and Convolutional Neural Networks (CNN), have been widely used in this domain. These algorithms have been applied to various electromechanical systems, with a focus on equipment like motors and faults like bearing failures. However, there is still room for improvement in the optimal design of the DL architecture and the integration of different DL methods. Overall, machine learning algorithms have proven to be effective in predicting maintenance needs for mechanical engineering systems, but further research is needed to enhance their performance and applicability.
What is the state-of-the-art maintenance for mining mobile machines?5 answersThe state-of-the-art maintenance for mining mobile machines involves a shift from preventive maintenance to predictive maintenance. Preventive maintenance is the main form of maintenance in the mining industry, but with the increase in sensor data and wireless infrastructure, predictive maintenance practices are being applied. This involves utilizing vibration measurement devices and machine learning algorithms to assess the real-time condition of the machines and generate predictive maintenance plans. By continuously analyzing data obtained from various machines in real time, valuable information and expertise can be extracted to avoid machinery/equipment failure. Additionally, the use of modern information systems and diagnostic parameters allows for setting a reasonable service time based on the state of the machines. Overall, the state-of-the-art maintenance for mining mobile machines involves a combination of sensor data, wireless communication, machine learning, and diagnostic parameters to enable predictive maintenance and avoid unplanned downtimes.
How to use Sarima for predictive maintenance?5 answersSARIMA models can be used for predictive maintenance by following a structured process. First, a SARIMA model is selected and configured with predefined order and seasonal order parameters. Then, the model is applied to known values in a time series to predict future values. Multiple executions of the SARIMA model are performed in parallel, and the precision of each parameter is determined based on past observations. This eliminates the need for specific algorithms to estimate parameters and improves the accuracy of future predictions. The proposed approach achieves better precision and performance compared to traditional SARIMA execution, with up to 10.77% better accuracy and faster processing times.
What are the most common causes of machine failure?5 answersThe most common causes of machine failure include wear and tear during operation, such as bearing defects, gear damage, misalignment, imbalance, and mechanical looseness. Inherent design faults, resulting from improper installation, poor servicing, bad workmanship, and structural dynamics design deficiency, also contribute to machine failures. Time-based failures, related to corrosion, erosion, wear, and creep mechanisms, can be avoided or mitigated through preemptive maintenance activities. Random failures, controlled by human error, process upsets, or multiple unrelated external factors, are another common cause of machine failure. Insulation system failure, including moisture, vibration, over-temperature, system surges, over-speed, and earthing practices, as well as mechanical failure, such as bearings failure, misalignment, air gap, out of balance, critical speed and resonance, and corrosion, are also significant causes of machine failure.
What are the challenges of machine maintenance in The Gambia?4 answersMachine maintenance in The Gambia faces several challenges. One of the main challenges is the high cost of maintenance, which accounts for a significant portion of the total production cost. Another challenge is the lack of materials, human resources, and funding from the government, which leads to inadequate maintenance of machines, particularly in the healthcare sector. Additionally, there are challenges in enforcing preventive measures, such as social distancing, due to socio-cultural factors. In low-resource settings, like The Gambia, providing a reliable supply of oxygen for medical treatment is also a major challenge. These challenges highlight the need for preventive maintenance strategies and investment in resources to ensure the proper functioning and longevity of machines in various sectors in The Gambia.