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The often-occurring short-term orders of manufactured products require a high machine availability. This requirement increases the importance of predictive maintenance solutions for bearings used in machines. There are, among others, hybrid solutions that rely on a physical model. For their usage, knowing the different degradation stages of bearings is essential. This research analyzes the underlying failure mechanisms of these stages theoretically and in a practical example of the well-known FEMTO dataset used for the IEEE PHM 2012 Data Challenge to provide this knowledge. In addition, it shows for which use cases the usage of low-frequency accelerometers is sufficient. The analysis provides that the degradation stages toward the end of the bearing life can also be detected with low-frequency accelerometers. Further, the importance of high-frequency accelerometers to detect bearing faults in early degradation stages is pointed out. These aspects have not been paid attention to by industry and research until now, despite providing a considerable cost-saving potential.
As cyber-attacks and functional safety requirements increase in Operational Technology (OT), implementing security measures becomes crucial. The IEC/IEEE 60802 draft standard addresses the security convergence in Time-Sensitive Networks (TSN) for industrial automation.We present the standard’s security architecture and its goals to establish end-to-end security with resource access authorization in OT systems. We compare the standard to our abstract technology-independent model for the management of cryptographic credentials during the lifecycles of OT systems. Additionally, we implemented the processes, mechanisms, and protocols needed for IEC/IEEE 60802 and extended the architecture with public key infrastructure (PKI) functionalities to support complete security management processes.
Training deep neural networks using backpropagation is very memory and computationally intensive. This makes it difficult to run on-device learning or fine-tune neural networks on tiny, embedded devices such as low-power micro-controller units (MCUs). Sparse backpropagation algorithms try to reduce the computational load of on-device learning by training only a subset of the weights and biases. Existing approaches use a static number of weights to train. A poor choice of this so-called backpropagation ratio limits either the computational gain or can lead to severe accuracy losses. In this paper we present TinyProp, the first sparse backpropagation method that dynamically adapts the back-propagation ratio during on-device training for each training step. TinyProp induces a small calculation overhead to sort the elements of the gradient, which does not significantly impact the computational gains. TinyProp works particularly well on fine-tuning trained networks on MCUs, which is a typical use case for embedded applications. For typical datasets from three datasets MNIST, DCASE2020 and CIFAR10, we are 5 times faster compared to non-sparse training with an accuracy loss of on average 1%. On average, TinyProp is 2.9 times faster than existing, static sparse backpropagation algorithms and the accuracy loss is reduced on average by 6 % compared to a typical static setting of the back-propagation ratio.
Efficient, secure and reliable communication is a major precondition for powerful applications in smart metering and smart grid. This especially holds true for the so called primary communication in the Local Metrological Network (LMN) between meter and data collector, as the LMN comes with the most stringent requirements with regard to cost, range, as well as bandwidth and energy efficiency. Until today, LMN field tests are operated all over the world. In these installations, however, energy autarkic systems play a marginal role. This contribution describes the results of the framework 7 (FP 7) WiMBex project (“Remote wireless water meter reading solution based on the EN 13757 standard, providing high autonomy, interoperability and range”). In this project an energy autarkic water meter was developed and tested, which follows the specification of the Wireless M-Bus protocol (EN 13757). The complete system development covers the PCB with the RF transceiver and the microcontroller, the energy converter and storage, and the software with the protocol. This contribution especially concentrates on the design, the development and the verification of the routing protocol. The routing protocol is based on the Q mode of EN13757-5 (Wireless M-Bus) and was extended by an additional energy state related parameter. This extension is orthogonal to the existing protocol and considers both the charge level and the charge characteristics (rate of occurrences, intensity). The software was implemented in NesC under the operating system TinyOS. The system was verified in an automated test bed and in field tests in UK and Ireland.
In the last decade, IPv6 over Low power Wireless Personal Area Networks, also known as 6LoWPAN, has well evolved as a primary contender for short range wireless communication and holds the promise of an Internet of Things, which is completely based on the Internet Protocol. In the meantime, various 6LoWPAN implementations are available, be it open source or commercial. One of these implementations, which was developed by the authors' team, was tested on an Automated Physical Testbed for Wireless Systems at the Laboratory Embedded Systems and Communication Electronics of Offenburg University of Applied Sciences, which allows the flexible setup and full control of arbitrary topologies. It also supports time-varying topologies and thus helps to measure performance of the RPL implementation. The results of the measurements show a very good stability and short-term and long-term performance also under dynamic conditions. In addition, it can be proven that the performance predictions from other papers are consistent with real-life implementations.
Temperature regulation is an important component for modern high performance single -core and multi-core processors. Especially high operating frequencies and architectures with an increasing number of monolithically integrated transistors result in a high power dissipation and - since processor chips convert the consumed electrical energy into thermal energy - in high operating temperatures. High operating temperatures of processors can have drastic consequences regarding chip reliability, processor performance, and leakage currents. External components like fans or heat spreaders can help to reduce the processor temperature - with the disadvantage of additional costs and reduced reliability. Therefore, software based algorithms for dynamic temperature management are an attractive alternative and well known as Dynamic Thermal Management (DTM). However, the existing approaches for DTM are not taking into account the requirements of real-time embedded computing, which is the objective in the given project. The first steps are the profiling and the thermal modeling of the system, which is reported in this paper for a Freescale i. MX6Q quad-core microprocessor. An analytical model is developed and verified by an extensive set of measurement runs.
WirelessHART protocol was specifically designed for real-time communication in the wireless sensor networks domain for industrial process automation requirements. Whereas the major purpose of WirelessHART is the read-out of sensors with moderate real-time requirements, an increasing demand for integration of actuator applications can be observed. Therefore, it must be verified that the WirelessHART protocol gives sufficient support to real-time industry requirements. As a result, the delay of especially burst and command messages from actuator and sensor nodes to the gateway and vice versa must be analyzed. In this paper, we implemented a WirelessHART network scenario in WirelessHART simulator in NS-2 [8], simulated and analyzed its time characteristics under ideal and noisy conditions. We evaluated the performance of the implementation in order to verify whether the requirements of industrial process and control can be met. This implementation offers an early alternative to expensive test beds for WirelessHART in real-time actuator applications.
Although short range wireless communication explicitly targets local and very regional applications, range continues to be an extremely important issue. The range directly depends on the so called link budget, which can be increased by the choice of modulation and coding schemes. Especially, the recent transceiver generation comes with extensive and flexible support for Software Defined Radio (SDR). The SX127x family from Semtech Corp. is a member of this device class and promises significant benefits for range, robust performance, and battery lifetime compared to competing technologies. This contribution gives a short overview into the technologies to support Long Range (LoRa ™), describes the outdoor setup at the Laboratory Embedded Systems and Communication Electronics of Offenburg University of Applied Sciences, shows detailed measurement results and discusses the strengths and weaknesses of this technology.
To deal with frequent power outages in developing countries, people turn to solutions like uninterruptible power supply (UPS), which stores electric energy during normal operating hours and use it to meet energy needs during rolling blackout intervals. Locally produced UPSs of poorer power quality are widely accessible in the marketplaces, and they have a negative impact on power quality. The charging and discharging of the batteries in these UPSs generate significant amount of power losses in weak grid environments. The Smart-UPS is our proposed smart energy metering (SEM) solution for low voltage consumers that is provided by the distribution company. It does not require batteries, therefore there is no power loss or harmonic distortion due to corresponding charging and discharging. Through load flow and harmonic analysis of both traditional UPS and Smart-UPS systems on ETAP, this paper examines their impact on the harmonics and stability of the distribution grid. The simulation results demonstrate that Smart-UPS can assist fixing power quality issues in a developing country like Pakistan by providing cleaner energy than the battery-operated traditional UPSs.
The importance of machine learning has been increasing dramatically for years. From assistance systems to production optimisation to support the health sector, almost every area of daily life and industry comes into contact with machine learning. Besides all the benefits that ML brings, the lack of transparency and the difficulty in creating traceability pose major risks. While there are solutions that make the training of machine learning models more transparent, traceability is still a major challenge. Ensuring the identity of a model is another challenge. Unnoticed modification of a model is also a danger when using ML. One solution is to create an ML birth certificate and an ML family tree secured by blockchain technology. Important information about training and changes to the model through retraining can be stored in a blockchain and accessed by any user to create more security and traceability about an ML model.