If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
Recent trends focusing on Industry 4.0 concept and smart manufacturing arise a data-driven fault diagnosis as key topic in condition-based maintenance. Fault diagnosis is considered as an essential task in rotary machinery since possibility of an early detection and diagnosis of the faulty condition can save both time and money. Traditional data-driven techniques of fault diagnosis require signal processing for feature extraction, as they are unable to work with raw signal data, consequently leading to need for expert knowledge and human work. The emergence of deep learning architectures in condition-based maintenance promises to ensure high performance fault diagnosis while lowering necessity for expert knowledge and human work. This paper presents developed technique for deep learning-based data-driven fault diagnosis of rotary machinery. The proposed technique input raw three axis accelerometer signal as high-definition image into deep learning layers which automatically extract signal features, enabling high classification accuracy.
You shouldn't let anyone see you enter your phone's login password -- but there could also be a danger from hackers hearing it over your smartphone's microphone. Experts from England and Sweden have shown how hacked microphones can be used to decode the sound of typing on a smartphone screen into the keys pressed. In a test, their algorithm could correctly guess 31 out of 50 four-digit login pins in just 10 attempts based on recordings made of the participants as they typed. These potential attacks would likely begin with the accidental download of malicious software -- so users should keep themselves safe by only using trusted apps. Limiting microphone access to only those apps that need it will also help to make your smartphone more secure.
Drones and self-driving cars may soon come with'spidey' senses. That's according to engineers in America, who believe the unmanned machines would benefit from sensory detectors similar to those often seen in arachinds. Specifically, they're referring the hairs on a spider's legs, which are linked to special neurons called mechanoreceptors, which flag-up danger through vibrations. If machines had similar characteristics, they'd be able to navigate more effectively in dangerous environments. Until now, sensor technology hasn't always been able to process data fast enough, or as smoothly, as nature.
In the context of Industry 4.0, data management is a key point for decision aid approaches. Large amounts of manufacturing digital data are collected on the shop floor. Their analysis can then require a large amount of computing power. The Big Data issue can be solved by aggregation, generating smart and meaningful data. This paper presents a new knowledge-based multi-level aggregation strategy to support decision making. Manufacturing knowledge is used at each level to design the monitoring criteria or aggregation operators. The proposed approach has been implemented as a demonstrator and successfully applied to a real machining database from the aeronautic industry. Decision Making; Machining; Knowledge based system
In this paper, we introduce the concept of intermittent learning, which enables energy harvested computing platforms to execute certain classes of machine learning tasks. We identify unique challenges to intermittent learning relating to the data and application semantics of machine learning tasks. To address these challenges, we devise an algorithm that determines a sequence of actions to achieve the desired learning objective under tight energy constraints. We further increase the energy efficiency of the system by proposing three heuristics that help an intermittent learner decide whether to learn or discard training examples at run-time. In order to provide a probabilistic bound on the completion of a learning task, we perform an energy event-based analysis that helps us analyze intermittent learning systems where the uncertainty lies in both energy and training example generation. We implement and evaluate three intermittent learning applications that learn the air quality, human presence, and vibration using solar, RF, and kinetic energy harvesters, respectively. We demonstrate that the proposed framework improves the energy efficiency of a learner by up to 100% and cuts down the number of learning examples by up to 50% when compared to state-of-the-art intermittent computing systems without our framework.
For all the advances in medical diagnostics made over the last two centuries of modern medicine, from the ability to peer deep inside the body with the help of superconducting magnets to harnessing the power of molecular biology, it seems strange that the enduring symbol of the medical profession is something as simple as the stethoscope. Hardly a medical examination goes by without the frigid kiss of a stethoscope against one's chest, while we search the practitioner's face for a telltale frown revealing something wrong from deep inside us. The stethoscope has changed little since its invention and yet remains a valuable if problematic diagnostic tool. Efforts have been made to solve these problems over the years, but only with relatively recent advances in digital signal processing (DSP), microelectromechanical systems (MEMS), and artificial intelligence has any real progress been made. This leaves so-called smart stethoscopes poised to make a real difference in diagnostics, especially in the developing world and under austere or emergency situations.
Condition monitoring is central to the efficient operation of wind farms due to the challenging operating conditions, rapid technology development and high number of aging wind turbines. In particular, predictive maintenance planning requires early detection of faults with few false positives. This is a challenging problem due to the complex and weak signatures of some faults, in particular of faults occurring in some of the drivetrain bearings. Here, we investigate recently proposed condition monitoring methods based on unsupervised dictionary learning using vibration data recorded over 46 months under typical industrial operations, thereby contributing novel test results and real world data that is made publicly available. Results of former studies addressing condition--monitoring tasks using dictionary learning indicate that unsupervised feature learning is useful for diagnosis and anomaly detection purposes. However, these studies are based on small sets of labeled data from test rigs operating under controlled conditions that focus on classification tasks, which are useful for quantitative method comparisons but gives little information about how useful these approaches are in practice. In this study dictionaries are learned from gearbox vibrations in six different turbines and the dictionaries are subsequently propagated over a few years of monitoring data when faults are known to occur. We perform the experiment using two different sparse coding algorithms to investigate if the algorithm selected affects the features of abnormal conditions. We calculate the dictionary distance between the initial and propagated dictionaries and find time periods of abnormal dictionary adaptation starting six months before a drivetrain bearing replacement and one year before the resulting gearbox replacement. We also investigate the distance between dictionaries learned from geographically nearby
In this survey paper, we systematically summarize the current literature on studies that apply machine learning (ML) and data mining techniques to bearing fault diagnostics. Conventional ML methods, including artificial neural network (ANN), principal component analysis (PCA), support vector machines (SVM), etc., have been successfully applied to detecting and categorizing bearing faults since the last decade, while the application of deep learning (DL) methods has sparked great interest in both the industry and academia in the last five years. In this paper, we will first review the conventional ML methods, before taking a deep dive into the latest developments in DL algorithms for bearing fault applications. Specifically, the superiority of the DL based methods over the conventional ML methods are analyzed in terms of metrics directly related to fault feature extraction and classifier performances; the new functionalities offered by DL techniques that cannot be accomplished before are also summarized. In addition, to obtain a more intuitive insight, a comparative study is performed on the classifier performance and accuracy for a number of papers utilizing the open source Case Western Reserve University (CWRU) bearing data set. Finally, based on the nature of the time-series 1-D data obtained from sensors monitoring the bearing conditions, recommendations and suggestions are provided to applying DL algorithms on bearing fault diagnostics based on specific applications, as well as future research directions to further improve its performance.
It might be some time before NASA's InSight lander starts churning out data, but the new Mars explorer is steadily trucking on with its preparations. A new animation shared by the space agency shows InSight getting its grapple instrument in place, in a process that looks much like the age-old cup-and-ball game. InSight has spent the last two months slowly setting up its instruments and conducting equipment health assessments ahead of its mission to listen for underground marsquakes. In the meantime, the lander's social media account has attracted the attention of many space enthusiasts – including Star Trek actor William Shatner, who questioned the source of a peculiar blue light that has appeared in some of the photos. NASA's InSight rover tries out its grappler on Mars Hulu's latest documentary looks at the failed music Fyre festival Model who'turned black' claims she will have a'black baby' A new animation shared by the space agency shows InSight getting its grapple instrument in place, in a process that looks much like the age-old cup-and-ball game.
NASA's InSight lander is leaning in for a better listen of Mars' underground tremors. The robotic explorer placed its seismometer on the surface at the end of last month, and is now getting even closer'for a better connection with Mars.' This will help its instruments pick up fainter signals that may otherwise have been missed. NASA's InSight lander is leaning in for a better listen of Mars' underground tremors. The robotic explorer placed its seismometer on the surface at the end of last month, and is now getting even closer'for a better connection with Mars.' Before and after images show its instrument at its lowest position yet Days prior, InSight leveled out its seismometer and adjusted the internal sensors ahead of lowering everything down toward the ground.