Unexpected downtime costs industries billions of dollars each year, and the challenges of aging assets combined with many experienced professionals at or near retirement age have companies looking at the promises of IoT technology for answers.

China Steel Corporation (CSC) is the largest integrated steel maker in Taiwan. Our goal is to enhance facility maintenance and management efficiency. First and foremost, to achieve this goal we know we need to understand the tradeoffs between the set-up costs of machine condition monitoring (MCM) devices and the losses we experience due to unexpected faults. We also need to consider the critical pieces of equipment we own and the degree of difficulty involved in maintaining them. Effective MCM can help maintenance managers understand the condition of critical equipment in real time, which is a competitive advantage for steel manufacturers.Our first self-developed facility online monitoring and diagnosis system (FOMOS) was installed on the CSC production line in 1998. The system performed well for monitoring certain issues such as mill vibrations, but after a few years the system became less reliable. We found that the hardware and software had limited functionality. We knew we could build a smarter, more connected system that could use the latest advances in processing power, wireless networks, and software connectivity. We realized that it was fairly easy to develop an MCM system, but it was difficult to address specific challenges we faced.Our biggest challenge to address was how to detect equipment abnormalities at an early stage without creating a large number of false alarms. Since vibration is a relative condition indicator and strongly affected by system or structural dynamic rigidity and transmissibility, in real situations, it is common to find that two pieces of the same equipment at similar locations and operating conditions exhibit different vibration levels after several years of operation. Vibration signals can be a key indicator for information such as wear, imbalance, misalignment, impact load, and bearing fault by using different signal processing and algorithms.A conventional monitoring and diagnosis system uses the same or very few indicators and criteria to monitor and diagnose various machine conditions regardless of the nature of the operation of the machine. Therefore, these systems are prone to false alarms or missing alarms. To make it worse, with diverse monitored equipment, a complicated operation regime, and widespread applications in numerous process lines in an integrated steel factory, it is impractical to achieve our goals with only a few diagnosis experts. Thus, we discovered the best way to improve the effectiveness and efficiency of our MCM system was to introduce some artificial intelligence methods.

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.

The current state of condition monitoring requires manual measurements that are compounded with aging equipment and the retirement of knowledgeable personnel.

Delivering a high-performance, automated turf harvester that reliably and efficiently palletizes turf in a variety of farming conditions while increasing farm productivity, reducing machine operating costs, and providing a secure Internet gateway to remotely monitor and control the turf harvester.

Developing a control system for a vertical, floating wind turbine that can monitor the operating conditions of the turbine, control it to make sure it operates at its highest efficiency, gather and log data, and securely transfer the data to operators and developers anywhere in the world for analysis and continuous development.