Novel PHM concept for future use in safety relevant electronics for harsh environment Dr. Przemyslaw Jakub GROMALA (Robert Bosch GmbH)
Electronic systems are and will be the major factor that will increase the safety on the road. According to the World Health Organization up to 2020 it is expected that number of accidents will be reduced by 50%. This will be realized through introduction of more advanced safety relevant systems that will slowly take control as well as responsibility for steering the car. As of today the safety relevant systems such as ABS/ESP saved only in Germany 8500 lives, and prevent 260000 accidents. Rear-end collisions that are among the worst, can be reduced by 72% due to the emergency braking system. In the urban traffic, up to 40 km/h the Bosch emergency braking system can completely prevent collisions with stationary vehicles. It is expected that autonomous driving will completely revolutionize the transportation system that will finally lead to 0 casualties in 2050.
In order to fulfill social expectancy, the safety relevant electronics systems that will be used in future automotive industry must be more complex. The future ECUs used in self-driving cars will be typically smart systems of the 3rd generation, which will perform human like operations. The 3rd generation smart systems will act independently in respect to control and decision making. In addition, these systems will be able to self-testing, self-calibration and self-healing.
Last but not least, the concept of Internet of Things will bring the components that are traditionally developed for consumer electronic market under the engine hood. All these aspects will required new approach regarding reliability and quality assurance. It is already observed that the lifetime requirements for embedded electronics used in automotive increases from 15 towards 25 years and for avionics systems towards 35 years. At the same time the time of qualification test is expected to be reduced by 30% with the cost of reliability tests to be reduced by 25%.
All these challenges and requirements can be realized by development of the new reliability concept that is strongly supported through numerical simulation and product optimization at the very early development stage. In the seminar I will present the novel approach for reliability assessment of the future electronic control units and smarty systems. I will present concept of simulation driven design that we are using during the development phase as well as the application of the hybrid prognostics and health management concept for the future safety relevant electronic control modules.
Bearing Race Faults Classification using Simulation-generated Training Data and Feature Free Methods Dr. Cameron SOBIE, Carina FREITAS and Mike NICOLAI (Siemens)
Rotating machinery is central to transportation and power generation, and maximizing its uptime while minimizing unplanned maintenance is important from safety and economic perspectives. Roller bearings are ubiquitous components in such machinery and are very often the cause of failures; thus, condition monitoring of roller bearings is a topic of key interest to many industries. Historically accomplished using human expertise and experience to estimate the condition of a machine from a limited set of signals and curated statistical features, machine learning has become an effective tool in bearing fault identification with modern computing and algorithmic advances. In this regard, two major challenges exist: first, the availability of in-service data from bearings containing faults is often rare or difficult to obtain, and moreover, for machines being deployed to the field for the first time, no such data exists. Secondly, the extracted statistical measures (features) must be chosen to maximize the classifier accuracy (or another metric), and their selection of these to maximize accuracy can be a challenging task. We directly address these challenges with two separate approaches. With validation against four experimental datasets, we show that in the absence of recorded in-service data, machine learning algorithms trained using simulated bearing vibration signals (i.e. simulation-driven machine learning) can classify bearing race faults with greater accuracy than those trained using data collected from other machines. Next, we propose convolutional neural networks and nearest-neighbor dynamic time warping (NNDTW) as statistical feature-free methods to detect bearing race faults using a signal processing pipeline based on angle synchronous averaging. We show that these methods offer superior accuracy from the simulation-driven perspective and can predict an in-service wind turbine fault over one month before failure.
A Proposal for Applying Adaptive Wireless Communication System for Smart Factory Mr. Takashi SAKAKURA (Mitsubishi Electric Corporation)
Activities to deploying devices that connect Internet, said Internet of Things in the industrial domain are accelerating to improve efficiency, prevent down time led by
organizations such as Industry 4.0 and Industrial Internet Consortium. Moreover a trend in industrial domain that manufactures have to provide various and small amount of products let manufactures reorganize their manufacturing facilities frequently. In often the case, the most costly portion of the reorganization is re-cabling cost. Basing on this background, wireless communications are highly
desired especially devices that can operate without power line. In this study, we propose a wireless communication system that configure properties of the system such as carrier wave frequency, bandwidth, modulation, autonomously depending on application requirements and circumstance of radio wave propagation.