How to Do Machine Learning with Small Data? -- A Review from an Industrial Perspective

Artificial intelligence experienced a technological breakthrough in science, industry, and everyday life in the recent few decades. The advancements can be credited to the ever-increasing availability and miniaturization of computational resources that resulted in exponential data growth. However, because of the insufficient amount of data in some cases, employing machine learning in solving complex tasks is not straightforward or even possible. As a result, machine learning with small data experiences rising importance in data science and application in several fields. The authors focus on interpreting the general term of "small data" and their engineering and industrial application role. They give a brief overview of the most important industrial applications of machine learning and small data. Small data is defined in terms of various characteristics compared to big data, and a machine learning formalism was introduced. Five critical challenges of machine learning with small data in industrial applications are presented: unlabeled data, imbalanced data, missing data, insufficient data, and rare events. Based on those definitions, an overview of the considerations in domain representation and data acquisition is given along with a taxonomy of machine learning approaches in the context of small data

Inline Monitoring of Battery Electrode Lamination Processes Based on Acoustic Measurements

Due to the energy transition and the growth of electromobility, the demand for lithium-ion batteries has increased in recent years. Great demands are being placed on the quality of battery cells and their electrochemical properties. Therefore, the understanding of interactions between products and processes and the implementation of quality management measures are essential factors that requires inline capable process monitoring. In battery cell lamination processes, a typical problem source of quality issues can be seen in missing or misaligned components (anodes, cathodes and separators). An automatic detection of missing or misaligned components, however, has not been established thus far. In this study, acoustic measurements to detect components in battery cell lamination were applied. Although the use of acoustic measurement methods for process monitoring has already proven its usefulness in various fields of application, it has not yet been applied to battery cell production. While laminating battery electrodes and separators, acoustic emissions were recorded. Signal analysis and machine learning techniques were used to acoustically distinguish the individual components that have been processed. This way, the detection of components with a balanced accuracy of up to 83% was possible, proving the feasibility of the concept as an inline capable monitoring syste

Advanced methods in NDE using machine learning approaches

Machine learning (ML) methods and algorithms have been applied recently with great success in quality control and predictive maintenance. Its goal to build new and/or leverage existing algorithms to learn from training data and give accurate predictions, or to find patterns, particularly with new and unseen similar data, fits perfectly to Non-Destructive Evaluation. The advantages of ML in NDE are obvious in such tasks as pattern recognition in acoustic signals or automated processing of images from X-ray, Ultrasonics or optical methods. Fraunhofer IKTS is using machine learning algorithms in acoustic signal analysis. The approach had been applied to such a variety of tasks like quality assessment of gears in automotive industry; detection of cracks and impacts in aircraft materials, quality evaluation for musical instruments, determination of softness of tissue paper or condition monitoring of train wheels. The principal approach is based on acoustic signal processing with a primary and secondary analysis step followed by a cognitive system to create model data. Already in the second analysis steps unsupervised learning algorithms as principal component analysis are used to simplify data structures. In the cognitive part of the software further unsupervised and supervised learning algorithms will be trained. Later the sensor signals from unknown samples can be recognized and classified automatically by the same algorithms trained before. Recently the IKTS team was able to transfer the software for signal processing and pattern recognition on a small printed circuit board (PCB). Still the algorithms will be trained on an ordinary PC, however trained algorithms run on the hardware comprising of a Digital Signal Processor and a FPGA chip. The identical approach will be used for pattern recognition in image analysis of OCT pictures. Optical Coherence Tomography (OCT) is used to identify failures in planar ceramic components. After the depth related grey scale compensation and image noise reduction a sliding window will scan the picture to identify various failures in the ceramic material using machine learning algorithms. Again automated classification of the components is possible. These are just two examples how machine learning can be used in quality inspection and non-destructive testing. Some key requirements have to be fulfilled however. A sufficiently large set of training data, a high signal-to-noise ratio an optimized and exact fixation of components are key requirements to get useful results. So the well trained NDT expert is still required to develop and validate the concept. The automated testing can be done subsequently by the machine. It will be of high value to collect all test data and link it to any single component. By integrating the test data of many components along the value chain and even with field use data further optimization including lifetime and durability prediction based on big data becomes possible, even if components are used in different versions or configurations. This is the promise behind German „Industrie 4.0.

Vorrichtung und Verfahren zur Bestimmung eines medizinischen Gesundheitsparameters eines Probanden mittels Stimmanalyse

Eine Vorrichtung zur Bestimmung eines Gesundheitsparameters eines Probanden mittels Stimmauswertung umfasst eine Verarbeitungseinrichtung, die ausgebildet ist, um eine digitalisierte Sprechprobe des Probanden basierend auf individuellen Modellparametern auszuwerten, um eine Messinformation zu erhalten, die innerhalb eines Toleranzbereichs auf einem Momentanwert des Gesundheitsparameters des Probanden basiert, wobei die individuellen Modellparameter einen funktionalen Zusammenhang zwischen der Sprechprobe oder von der Sprechprobe abgeleiteten Sprechmerkmalen und einem zugeordneten, momentanen Gesundheitsparameter angeben

Acoustic Resonance Testing of Small Data on Sintered Cogwheels

Based on the fact that cogwheels are indispensable parts in manufacturing, we present the acoustic resonance testing (ART) of small data on sintered cogwheels for quality control in the context of non-destructive testing (NDT). Considering the lack of extensive studies on cogwheel data by means of ART in combination with machine learning (ML), we utilize time-frequency domain feature analysis and apply ML algorithms to the obtained feature sets in order to detect damaged samples in two ways: one-class and binary classification. In each case, despite small data, our approach delivers robust performance: All damaged test samples reflecting real-world scenarios are recognized in two one-class classifiers (also called detectors), and one intact test sample is misclassified in binary ones. This shows the usefulness of ML and time-frequency domain feature analysis in ART on a sintered cogwheel dataset

Monitoring of compressor operations - A machine learning approach

Compressors are important components in many industries. Proper monitoring technologies are very important and demanding. Failure of compressors could be very costly. The quality of compressor performance maps is very important for the operational availability of compressed air because there is a huge use of compressed air in almost every branch of industry. A remarkable percentage of the maintenance cost could be saved with a proper monitoring technology and maintenance program. Caused by the variety of compressor types and operational conditions complex superpositions of vibrational states occur which makes automated evaluation of faults demanding. In addition to vibration data the study has been extended to ultrasound frequencies using a new sensor technology. The broadband-ultrasound sensors and the diagnostic system cover a frequency range up to about 100 kHz enabling the simultaneous acquisition of vibration and ultrasound. The higher frequency range enables often an approach to early indications of faults. The extension of the sensor and signal processing methodology towards higher frequencies provides some advantages for the earlier prediction of operational states and lifetime of compressor components due to its sensitivity to small scale vibrations and turbulences caused by vibration effects. An increase of friction and micro-shocks, often an indicator for inappropriate operation, does not provide intense vibration. For this study, a screw type compressor has been equipped with a set of new broadband ultrasound sensors. For comparison and complementary purposes, vibration sensors have been placed. The use of ultrasound and advanced data technology has been demonstrated for different operational states over a longer period. It has been shown that ultrasound can be a promising tool for condition monitoring and fault diagnosis of screw compressors. Amongst the new sensor technology, advanced data processing on the basis of machine learning techniques such as deep neural networks provides an advanced diagnostic network

Towards an autarkic embedded cognitive user interface

With this paper we present an overview of an autarkic embedded cognitive user interface. It is realized in form of an integrated device able to communicate with the user over speech & gesture recognition, speech synthesis and a touch display. Semantic processing and cognitive behaviour control support intuitive interaction and help controlling arbitrary electronic devices. To ensure user privacy and to operate autonomously of network access all information processing is done on the device

Zerstörungsfreie Prüfung

1. Grundlagen // 2. Optische Verfahren / 2.1. Sichtprüfung / 2.2. Eindringprüfung / 2.3. Thermographie / 2.4. Optische Kohärenztomographie / 2.5. Laser-Speckle-Photometrie // 3. Elektromagnetische Verfahren / 3.1 Röntgenverfahren // 4. Elastodynamische Verfahren / 4.1. Ultraschallprüfung / 4.2. Resonanzprüfung / 4.3. Schallemissionsprüfun