Keeping aircraft and rail transport systems up and running requires a vast organization of equipment maintenance, safety regulations, schedule optimization, and management of staff and passengers. To be successful, a variety of information must be collected on many levels. The emergence of the IoT and telematics (discussed in our last blog) has recently enabled development of real-time monitoring and analyzation of integrated information in almost every faction of logistics and transportation. This technology allows consolidation of Big Data and is currently growing in use. The initial investment of system overhaul may be a hurdle, but the long-term benefits will ultimately outweigh the costs.
Airplanes have long utilized sensor packages that monitor and analyze usage data such as performance, fuel consumption, and vibration to create optimized maintenance schedules. The integration of telematics and the IoT with these systems can send data in real-time to a center where it can be filtered, stored, and analyzed. Relevant data can then be sent to the appropriate organizations for follow-up response. For example, if a part indicates imminent failure, the system would relay a signal to notify the equipment manufacturer, and the manufacturer would ship a new part from the factory before actual failure. In this way, maintenance can switch from a reactive to a proactive model, saving downtime and costs, and increasing safety.
For examples of real life applications in airlines, see the blog supplement.
Similar upgrades are occurring in railway management. Older infrastructure is being replaced with systems that interconnect trains with each other and with central communication hubs and data control centers. These systems improve track utilization and safety, and allow optimization of scheduling and maintenance. Examples of smart-connected rail include:
Generating Actions from Big Data
Data collection, storage, and analysis can provide information that can be used to diagnose and fix, as well as to predict, prevent, and strategize. Real-time, descriptive data from monitoring can be incoporated with AI to automate analysis and provide a highly efficient delivery of prescriptive information. Combined with machine learning, systems can perform appropriate actions in the absence of a person. Data can be quickly analyzed and employed to correct a problem, or prevent one from occurring.
Utilizing Big Data for aircraft and railways can be taken further to examine long-term trends in how to route traffic, best practices in maintenance, serving passengers, scheduling, etc. This requires storage of event data and its metadata. Trends over weeks, months, and years can be evaluated, and intelligent strategic policies can be implemented. Such storage and analysis requires the right software to create logical and relevant data storage, retrieval, and analysis based on chosen parameters.
Publish Date: December 21, 2017 6:32 PM
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