Seung Hyup Ryu - Engine and Machinery Research Institute, Hyundai Heavy Industries
Hyeonsook Yoon - Engine and Machinery Research Institute, Hyundai Heavy Industries
Hyundai Heavy Industries (HHI) is the world’s top shipbuilding company and makes a wide range of products, from general merchant ships to special vessels. We continue to solidify our competitive position by actively and efficiently meeting the various needs of our customers and the market demand for eco-friendly, energy-efficient vessels and marine special vessels through continuous technological innovation. HHI’s Engine and Machinery Division (HHI-EMD) is the world’s largest marine diesel engine builder with approximately 35 percent global market share. In 2016, the division reached a production milestone of 160 million brake horsepower in two-stroke engines and 10,000 Hyundai HiMSEN diesel and gas engines. HHI-EMD is continuing technological research on marine engines, related systems, and eco-friendly marine products (LNG fuel supply facilities, NOx reduction facilities) and is playing a pivotal role in technology for smart ecoships.
HiMSEN, HHI’s own medium-sized engine brand, is a four-stroke engine with a bore size of 170–540 mm and output power of 960 kW–25 MW. It is used as a generator engine on large ships and a propulsion engine on medium and small ships. HHI-EMD is researching the following, not only for research and development of eco-friendly and high-performance engines, but also to lead market trends in the digitization and intelligence of engines:
• Developing diagnostics for high-performance engines
• Using virtual engine HIL to develop engine diagnostics
• Implementing a data mining system for engine diagnosis and preventive maintenance
To build a smart ecoship, we must develop engine diagnostic equipment such as an engine combustion performance analyzer and engine status/fault diagnosis equipment that can diagnose key parts of a ship. Developing these peripherals as well as the engine requires us to perform verification tests with peripheral devices alongside the performance tests of the engine itself. During the development and testing of diagnostics, we used the NI platform to solve two problems.
The first problem was developing engine diagnostics using the HIL system, instead of the conventional empirical testing method in which the engine had to be actually running to test the diagnostics. The second problem was sending the data collected from the engine monitoring system, combustion analysis system, engine control system, and more to the data mining system for real-time analysis.
Developing Diagnostics for High-Performance Engines
To develop a high-performance engine, we must develop onboard diagnostics devices (for example, HiCAS, HiEMS) that collect real-time operation and status information of the engine. Understanding the characteristics of the engine requires resampling the pick-up pulse signal with an angle resolution of up to 0.1 degree in an engine operating at several hundred to several thousand RPM. Therefore, we needed analytical equipment capable of performing interpolation operations of synchronized signals at processing speeds of several tens of µs per cylinder. However, existing equipment could not calculate the angular data required for each cylinder at high speeds.
The development of HICAS, a DAQ system that uses the NI 9215 module, the NI 9411 module, and the FPGA-based cRIO-9035 chassis, made it possible for us to analyze engine speed, in-cylinder pressure, pressure in the intake and exhaust system, and more. We could analyze in real time and send the status information of the engine to the host controller. This helped us diagnose the engine performance and the status of key parts by thermodynamic analysis.
We can use HiCAS for real-time analysis of dynamic data such as:
- In-cylinder peak pressure
- Indicated mean effective pressure/cycle-to-cycle variation
- Cylinder-to-cylinder distribution
- Cyclic moving parts fault diagnosis
Virtual Engine HILs for Development of Engine Diagnostics
The most difficult part of developing the onboard diagnostics devices described above was the verification testing. Developing the peripherals as well as the engine required us to perform verification tests with peripheral devices alongside the performance tests of the engine itself. We also need to validate and analyze the results of tests performed under various conditions.
When we perform verification testing for a HiMSEN engine, the estimated fuel cost alone is about $2,000 USD per hour. Including the direct costs and expenses incurred by engine operation such as lubricating oil and other indirect costs such as the personnel associated with testing and safety, the total cost easily exceeds $22,000 USD per day. In addition, verification testing involves many difficulties, including the high risks of the repetitive tests to ensure reliability due to the risk of safety and environmental problems associated with the operation of mid- to large-sized engines, tests for various operating conditions, and various failure simulations for the analysis of peripheral connectivity tests. Also, simultaneous installation and testing of multiple models is difficult, as medium- and large-sized engines have significant environmental constraints on installation location and operation.
Therefore, to minimize the verification testing, we enhanced the measurement and analysis process to improve test data utilization by applying virtual verification and data mining techniques based on HIL simulation, which uses a model-based design process.
As seen in the automobile industry’s massive recalls associated with electronic control system problems, we must secure control stability and reliability for electronic controller products because control errors can cause serious problems for the entire engine system. Therefore, the electronically controlled engine requires sufficient verification to effectively prevent problems that may be caused by the controller. The HIL evaluation technique is quite useful for this purpose. The HIL evaluation technique links the rig test of the actual product to the simulation environment based on numerical analysis. We can extend part-by-part testing to the final product’s perspective. Furthermore, since we can perform simulations to evaluate extreme testing environments, errors that are unlikely to occur, and simple repetitive tests, we can significantly reduce test cost and time. We can use VeriStand software in real-time testing to apply to numerical models of various modeling environments, which ensures fewer restrictions on the model development for engines and subsystems and a high level of compatibility.
This engine model works in real time through an NI PXI system, synchronized with the high-speed signal sensor simulation software on the multifunction FPGA board, transmitting the signal to the physical quantity signal I/O board. We installed analog I/O and digital I/O modules to receive signals from the external controllers and to simulate the signals of various types of sensors mounted on the engine. Also, by using LabVIEW FPGA Module and various simulation software development applications, we could carry out test evaluations of the failure modes, which are difficult to verify in actual engine-mounted testing conditions of the control and analysis equipment development process. With the development of engine diagnosis equipment through virtual verification based on the HIL system, we could shorten the development time from over three years to one year and accelerate the time-to-market.
Implementation of Data Mining System for Engine Diagnosis and Preventive Maintenance
To minimize the continuing maintenance cost of key parts, we needed a data management platform that could manage more than 600 channels of data collected from the engine and engine peripherals and implement a preventive maintenance system.
To solve this problem, we developed and applied data analysis software based on the LabVIEW Real-Time Module to perform thermodynamic and physical analysis of acquired data in real time. By simultaneously measuring the engine’s main measurement values with various physical quantities using CompactRIO, and by analyzing measurement data in real time and classifying and storing valid data, we could improve data quality and quantitative reduction. Also, we improved the convenience of status monitoring by implementing a user-monitoring interface using LabVIEW. We improved data management and post-utilization through quantitative reduction of data. We used NI InsightCM™ software for database management and to establish a basis for correlational analysis of various design variables and operating environment variables. We facilitated data trend analysis, such as tracking of the changes in operation characteristics due to aging of the engine, by tagging and categorizing the data accumulated over a long period of time. Also by developing and applying a monitoring and analysis library optimized for HHI using LabVIEW, we improved user friendliness, minimizing the learning curve of users with the new systems.
As a result, we could transition from the conventional data management method in which a team of engine experts (more than six people) managed data manually in Excel for three months, to an automated process of controlling, acquiring, and processing data from various measuring instruments using the NI InsightCM™ online status monitoring solution. The new method allows real-time monitoring of the data collected only with a single expert. We can shift the time and resources previously allocated to data collection to data analysis instead.
Development of Smart Ecoships in the Era of Industrial IoT Based on Big Data Analysis
The crux of the Internet of Things (IoT) and the Fourth Industrial Revolution is about using collected data in a meaningful way. Staying on the frontier of the shipbuilding industry requires the conventional practices of accurately measuring data from the key parts of the engine that produces power to move a huge ship (as large as four soccer fields), collecting the tones of data measured, and analyzing them in a meaningful way. For this purpose, HHI developed self-diagnosis equipment, which enables the measurement of data from key parts of the engine. HHI also implemented a data mining system to efficiently manage the data collected from such diagnostics and analyze and utilize them for preventive maintenance.
Starting with HiCAS, which is designed for the analysis of the engine’s core combustion performance, we plan to use our own technologies in the development of other advanced engine diagnostics that can diagnose the status change due to aging of the engine, predict failures, and facilitate necessary preparations. We expect that we can apply HIL simulations, which have already been implemented, in the equipment development process to reduce the development period and cost by half or more, as opposed to over three years and millions of dollars per piece.
We can use this to collect and analyze the data of marine engines operating around the world in real time, detecting problems that may occur in advance, notifying the vessels concerned, and enabling preventive measures such as replacing the parts in a timely manner. Through such automated monitoring systems, we can drastically reduce the effective maintenance and operating expenses of the high-performance engines.
The medium- and large-sized engine and engine diagnostics can be used not only on ships, but also on packaged power stations. They are planned for use in the development of smart grid systems in developing countries such as Chile, Cuba, and Nigeria.
Seung Hyup Ryu
Engine and Machinery Research Institute, Hyundai Heavy Industries
Tel: +82 52 203 8962