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Exploring Preventive Maintenance Systems in the Transportation Industry
Maintenance modernization has become critical to keeping aircraft, railways and fleets operating safely while being cost efficient. Condition-based monitoring and predictive maintenance are providing organizations in the transportation industry the capabilities for detecting when issues will arise, allowing maintenance engineers to circumvent breakdowns and downtime.
Maintenance in the transportation industry has always been somewhat complicated. Maintenance technicians and engineers are tasked with balancing the frequent and safe operation of locomotive machines with maximized uptime while reducing unexpected downtime. They are typically tasked with doing so with a fixed maintenance budget.
However, the transportation industry is increasingly embracing the fact that machines and their components hold valuable data that can not only optimize maintenance, but that can also make the machines much more reliable. They are using condition-based monitoring and predictive maintenance solutions like Odysight.ai®’s image-based AI platform to help capture critical data in typically difficult-to-access areas of the machinery. This data, some of which can be obtained by closely monitoring the condition of tires, brakes or engines, can be analyzed to determine when breakdowns are most likely to occur.
This approach to maintenance in the transportation industry is effective. According to one estimate regarding ground fleet maintenance in the logistics industry, the foresight enabled by condition-based monitoring and predictive maintenance can improve vehicle uptime by as much as 25 percent while saving up to $2,000 per vehicle per year. This makes the data that is collected and the tools used to obtain it almost invaluable.
Conventional Transportation Industry Maintenance Falters
The traditional approach to maintenance in the transportation industry has become disadvantageous for multiple reasons:
● It is too rigid, relying too heavily on scheduled maintenance (often determined by manufacturers’ instructions or mileage-based requirements).
● It is inefficient at combating breakdowns that can occur between regularly scheduled maintenance work.
● It attempts to, but does not adequately address the fact that the actual condition of equipment can be determined by many different factors, such as the temperature of the operating environment, the condition of nearby components, whether the machine is being operated properly, etc. It either falls short, resulting in breakdowns and downtime, or it over accommodates, such as by mandating the replacement of perfectly serviceable components.
In the past, routine inspections and maintenance actions were necessary to maximize the working life of locomotive machinery and their components. But with the availability of real-time data and predictive analytics to gain actionable insights from that data, there is no need for the transportation industry to rely on inefficient maintenance strategies.
Condition-Based Monitoring and Predictive Maintenance means Better Maintenance in the Transportation Industry
The ultimate goals of applying maintenance measures are less breakdowns, more uptime and less reliance on regularly scheduled inspections and maintenance. By using condition-based monitoring and predictive maintenance systems in the transportation industry, maintenance service is performed only when it is actually required.
In effective predictive maintenance strategies, condition-based monitoring ties machine maintenance to real-time performance metrics, resulting in maintenance that is much more tailored and efficient than the reliance on maintenance schedules. For example, in rail transport, Odysight.ai’s micro-visualization technology can be used to monitor and assess locomotives’ brake pads wear. I. It can be used for aviation maintenance to examine engine components or to assess the condition of landing gear. Fleet operators can use its supreme visualization capabilities to inspect engines for signs of belt wear or to examine leaks.
This data obtained in real-time during normal operations, along with the other various types of relevant data from various sources, is necessary to provide real-time insights on the actual, current condition and functionality of a machine. With this type of input, the appropriate maintenance measures can be taken to prevent any impending breakdowns or plan for the necessary downtime to replace critical components.
The Impact of Optimized Maintenance in the Transportation Industry
What are the end results of condition-based monitoring and predictive maintenance in the transportation industry?
● Minimized Vehicle Downtime. Organizations like airlines, trucking fleets, railway companies or any other organization that rely on vehicles in daily operations have to be able to ensure that they can operate properly and safely. They can decrease the costs associated with vehicle downtime by using condition-based monitoring and predictive maintenance to detect the early signs of breakdowns, determine the root cause of the impending breakdown and apply the maintenance measures that directly address the fault.
● Better Spare Parts Management. Being able to predict when breakdowns are likely to occur means that organizations will know what parts are needed for an upgrade or repair. Organizations can exercise a more efficient spare part management model that ensures replacements are already on hand exactly when they are needed.
● Increased Efficiency of Vehicles and Operators. Better maintenance improves the entire repair in the ecosystem of an organization. Individual profiles can be created for the vehicles in a fleet so that maintenance engineers can more easily conduct repairs and enact predictive maintenance measures to avoid issues. Organizations will not have to decommission their vehicles for extended periods of time and operators will have access to properly working machines.
Odysight.ai Provides Maintenance in the Transportation Industry
The practice of addressing maintenance or repair issues only once there are failures or complete breakdowns is no longer feasible for any organization. As in other industries, organizations in the transportation industry have discovered that they can reduce unexpected breakage and downtime while minimizing unnecessary component replacements by addressing the actual needs of their equipment. They can know when their vehicles require maintenance far in advance using the right maintenance strategies and tools.