Asset Health Management (AHM)
Overview
Asset Health Management refers to the process of analyzing the health of an asset as determined by operational requirements. The health of an asset in itself relates to the asset's utility, its need to be replaced, and its need for maintenance. It can be broken down into three key components: 1) Monitoring: Tracking the current operating status of the asset. 2) Diagnostic Analysis: Comparing real-time data to historical data in order to detect anomalies. 3) Prognostic Analysis: Identifying and prioritizing specific actions to maximize the remaining useful life of the asset based on analysis of real-time and historical data.
Applicable Industries
- Automotive
- Transportation
Applicable Functions
- Discrete Manufacturing
- Maintenance
Market Size
From 2013 to 2022, the market for overall asset efficiency improvements potentially accumulates to USD 2.5 trillion.
Source: Cisco
Case Studies.
Case Study
Remote Condition Monitoring for London Underground
London Underground serves 1.7 billion passengers per year and the Victoria Line accounts for 213 million of those journeys. The line carries 89.1 million passengers per year in the peak service, offering the most intensive service on the underground network. Over the past eight years, a £1 billion investment programme upgraded and replaced the Victoria Line’s rolling stock and signaling and control systems to deliver a service capable of running more than 33 trains per hour. The new signalling system uses 385 Jointless Track Circuits (JTCs) to detect train position, maintain safe train separation and deliver train headways capable of meeting an extremely demanding timetable. Track circuits are the sole means of train detection and play a critical role in the safe and reliable operation of the railway; however, no provision was made for any condition monitoring during the design and installation. Because of the critical nature of the asset, a failed track circuit has a major impact on the service and constitutes the biggest cause of passenger disbenefit on the Victoria Line, amounting to £1.5 million since their introduction (London Underground CuPID database for Track Circuit failures since 2012). The Victoria Line Condition Monitoring Team, made up of six professional engineers with rail, software, electrical, mechanical, network and engineering backgrounds, delivered the solution. National Instruments Silver Alliance Partner Simplicity AI supported the project by providing additional software consulting services. We used the company’s enormous breadth of expertise to deliver the system onto an operational railway within one year of the concept design. The scope of this project consisted of designing, integrating and installing an intelligent remote condition monitoring system that could perform real-time analysis of voltage and frequency for all 385 JTCs across a 45 km of deep tube railway to predict and prevent failures and subsequent loss of passenger service. We took advantage of the accuracy, reliability and flexibility of NI hardware and software to implement an innovative system to reduce the lost customer hours experienced on the Victoria Line. The system is forecast to reduce lost customer hours by 39,000 per year—an estimated £350,000 savings per year in passenger disbenefit.
Case Study
IIC Industrial Digital Thread (IDT) Testbed
Field engineers and service teams often lack data and digital insights needed to assess, troubleshoot, and determine work scope for the large industrial assets in performing corrective and preventative maintenance activities. QA engineers many times need to understand why a particular problem in the part is happening recurrently or why parts from suppliers don’t stack up well in the assemblies due to mismatch. The root cause is usually hidden in design, manufacturing processes, supply chain logistics or production planning. But without the right data and digital insights, it's hard to pinpoint. GOAL To collect information in the design, manufacturing, service, supply-chain setup and provide access to and intelligent analytics for industrial manufacturing and performance data, to identify the root cause easier. Such insights can improve not only service and owner/operator productivity, but also provide critical feedback to the design engineering and manufacturing operations teams for continuous improvement.