Webinar vogel it_so geht industrial edge analytics mittels machine learning_180525
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Webinar - So geht industrial edge analytics mittels machine learning - 180525 - Peter Seeberg
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Webinar vogel it_so geht industrial edge analytics mittels machine learning_180525
- 2. Copyright © 2018 Softing Industrial. All rights reserved. 2 Edge Computing Machine Learning Industrial Edge Analytics
- 3. Copyright © 2018 Softing Industrial. All rights reserved. 3 Machine Learning On the Edge auf dem Gerät
- 4. Copyright © 2018 Softing Industrial. All rights reserved. 4 Warum auf dem Gerät? Weil Sie Ihre privaten Bilder nicht in der Cloud haben möchten!
- 5. Copyright © 2018 Softing Industrial. All rights reserved. 5 Machine Learning On the Edge An der Maschine In der Anlage Industrial Analytics
- 6. Copyright © 2018 Softing Industrial. All rights reserved. 6 Warum an der Maschine? Weil Sie Ihre Produktionsdaten nicht in der Cloud haben möchten!
- 7. Copyright © 2018 Softing Industrial. All rights reserved. 7 Edge Computing… …bezeichnet, im Gegensatz zum Cloud Computing… die dezentrale Verarbeitung am Rand des Netzwerks
- 8. Copyright © 2018 Softing Industrial. All rights reserved. 8
- 9. Copyright © 2018 Softing Industrial. All rights reserved. 9
- 10. Copyright © 2018 Softing Industrial. All rights reserved. 10
- 11. Copyright © 2018 Softing Industrial. All rights reserved. 11
- 12. Copyright © 2018 Softing Industrial. All rights reserved. 12 Edge Computing Vorteile Sicherheit Geringeres Datenvolumen Skalierbarkeit (Streaming)
- 13. Copyright © 2018 Softing Industrial. All rights reserved. 13 ©Google,2016
- 14. Copyright © 2018 Softing Industrial. All rights reserved. 14 © Cornell University, Pranav Rajpurkar et all, 2017
- 15. Copyright © 2018 Softing Industrial. All rights reserved. 15 In 1959 definiert Arthur Samuel machine learning als ein "Studiengebiet, das Rechnern ermöglicht zu lernen, ohne explizit programmiert zu werden"
- 16. Copyright © 2018 Softing Industrial. All rights reserved. 16 Künstliche Intelligenz Artificial Intelligence Machine Learning Neurale Netze Deep Learning
- 17. Copyright © 2018 Softing Industrial. All rights reserved. 17
- 18. Copyright © 2018 Softing Industrial. All rights reserved. 18 ©Intel,2017
- 19. Copyright © 2018 Softing Industrial. All rights reserved. 19 ©Intel,2017
- 20. Copyright © 2018 Softing Industrial. All rights reserved. 20 In "The Adventure of the Silver Blaze," Sherlock Holmes famously solved a case not by discovering a clue - but by its absence. In that case, it was a dog that didn't bark, and lack of barking helped identify the culprit.
- 21. Copyright © 2018 Softing Industrial. All rights reserved. 21 Vom Regelbasierten… … zum Datengetriebenen
- 22. Copyright © 2018 Softing Industrial. All rights reserved. 22 Algorithmus EntscheidungDaten ©PeterSeeberg/Softing,2016
- 23. Copyright © 2018 Softing Industrial. All rights reserved. 23 Algorithmus EntscheidungDaten ©PeterSeeberg/Softing,2016
- 24. Copyright © 2018 Softing Industrial. All rights reserved. 24 ©FrankfurterAllgemeine,2018
- 25. Copyright © 2018 Softing Industrial. All rights reserved. 25 ©FrankfurterAllgemeine,2018
- 26. Copyright © 2018 Softing Industrial. All rights reserved. 26 Statistik………… Data Mining…. Machine Learning ©GartnerGroup,2013 Beschreibende Analyse Diagnostische Analyse Vorausschauende Analyse Vorschreibende Analyse Was ist Passiert? Warum ist es passiert? Was wird passieren? Was ist zu tun?
- 27. Copyright © 2018 Softing Industrial. All rights reserved. 27 Wir sind Experten für den digitalen Datenaustausch und die Bereitstellung von Informationen in Anwendungen der Fahrzeugindustrie, und der industriellen Produktion, sowie zur Sicherstellung der Leistungsfähigkeit von IT-Systemen
- 28. Copyright © 2018 Softing Industrial. All rights reserved. 28 Geschäftsbereich Automotive
- 29. Copyright © 2018 Softing Industrial. All rights reserved. 29 Geschäftsbereich IT Networks
- 30. Copyright © 2018 Softing Industrial. All rights reserved. 30 Geschäftsbereich Industrial
- 31. Copyright © 2018 Softing Industrial. All rights reserved. 31 Zeitreihen-Analyse
- 32. Copyright © 2018 Softing Industrial. All rights reserved. 32 Anomalie-Erkennung
- 33. Copyright © 2018 Softing Industrial. All rights reserved. 33 Prozessanalyse ©KIT,2017
- 34. Copyright © 2018 Softing Industrial. All rights reserved. 34 Ursachenanalyse
- 35. Copyright © 2018 Softing Industrial. All rights reserved. 35 Gesamtanlageneffektivität (OEE) Verfügbarkeit
- 36. Copyright © 2018 Softing Industrial. All rights reserved. 36 Gesamtanlageneffektivität (OEE) Verfügbarkeit Qualität
- 37. Copyright © 2018 Softing Industrial. All rights reserved. 37 Gesamtanlageneffektivität (OEE) Verfügbarkeit Qualität Leistung
- 38. Copyright © 2018 Softing Industrial. All rights reserved. 38 Machine Learning On the Edge An der Maschine In der Anlage Industrial Analytics
- 39. Copyright © 2018 Softing Industrial. All rights reserved. 39 MACHINE LEARNING WORKFLOW Raw Data Structured Data Candidate Model Golden Model Pre-processing & Transformations Model Training Model Validation Model Deployment
- 40. Copyright © 2018 Softing Industrial. All rights reserved. 40 Data Understanding Descriptive Statistics Analysis Zeiss - Softing knowledge exchange sessions Data visualization Data Pre- processing Data Analysis Data loaded in elasticsearch Initial data assessment: data availability Data visualization Data Cleaning: missing values management, abnormal values management (such as SSTTemp sensors) Data Transformation: normalization, dimensionality reduction, feature engineering System states identification using supervised learning (classification algorithm) Anomaly detection: univariate and multivariate Data Acquisition Data Analysis Pipeline
- 41. Copyright © 2018 Softing Industrial. All rights reserved. 41 Random Forest Model Accuracy Structured Data Candidate Model Golden Model Pre-processing & Transformations Model Training Model Validation Model Training Workflow Maximum Skewness Mean Kurtosis Percentiles Minimum Median PCA Raw Data
- 42. Copyright © 2018 Softing Industrial. All rights reserved. 42 Supervised Learning Unsupervised Learning Classification Regression Clustering Reinforcement Learning Dimensionality Reduction Semi-Supervised Learning
- 43. Copyright © 2018 Softing Industrial. All rights reserved. 43 Supervised Learning Unsupervised Learning Classification Regression Clustering Dimensionality Reduction Neurale Netze SAX SVM Decision Tree Autoregression Random Forest K-Means DB-SCAN Neurale Netze LSTM LOFPCASOM Min, Max, IQR
- 44. Copyright © 2018 Softing Industrial. All rights reserved. 44
- 45. Copyright © 2018 Softing Industrial. All rights reserved. 45 Datenbasierte Produktionsoptimierung Gesamtanlageneffektivität (OEE) • Verfügbarkeit
- 46. Copyright © 2018 Softing Industrial. All rights reserved. 46
- 47. Copyright © 2018 Softing Industrial. All rights reserved. 47 ©Google,2016
- 48. Copyright © 2018 Softing Industrial. All rights reserved. 48 Datenbasierte Produktionsoptimierung Gesamtanlageneffektivität (OEE) • Verfügbarkeit
- 49. Copyright © 2018 Softing Industrial. All rights reserved. 49 OSOA – On (Off) Site Offline Analytics
- 50. Copyright © 2018 Softing Industrial. All rights reserved. 50 Univariate Anomaly Detection • Analyze each sensor separately • Main techniques used: • Descriptive analysis • Distribution based analysis Multivariate Anomaly Detection • Analyze groups of correlated sensors for detecting complex anomalies • Main technique used: density based outlier detection Anomaly Detection
- 51. Copyright © 2018 Softing Industrial. All rights reserved. 51 High Contrast Subspace for Outlier Ranking Multivariate Anomaly Detection 1. High contrast subspace search 2. Local Outlier Factor
- 52. Copyright © 2018 Softing Industrial. All rights reserved. 52 Machine damper Actuator Position #2 Reticle Stage Stage axis Temperature (TRetZ3) Multivariate Anomaly Detection
- 53. Copyright © 2018 Softing Industrial. All rights reserved. 53 Univariate Anomaly Detection
- 54. Copyright © 2018 Softing Industrial. All rights reserved. 54 Univariate Anomaly Detection
- 55. Copyright © 2018 Softing Industrial. All rights reserved. 55
- 56. Copyright © 2018 Softing Industrial. All rights reserved. 56 Datenbasierte Produktionsoptimierung Gesamtanlageneffektivität (OEE) • Verfügbarkeit
- 57. Copyright © 2018 Softing Industrial. All rights reserved. 57 Conditional Monitoring
- 58. Copyright © 2018 Softing Industrial. All rights reserved. 58 Univariate CLUSTER BASED TECHNIQUS K-Means Local Outlier Factor STATISTICAL BASED TECHNIQUES Rule based PCA Regression Probability Distribution Based CLASSIFICATION BASED TECHNIQUES One Class SVM Isolation Forest Multivariate Algorithm Selection
- 59. Copyright © 2018 Softing Industrial. All rights reserved. 59 Structured Data Candidate Model Golden Model Pre-processing & Transformations Model Training Model Validation Model Deployment SSTTempZs TRetX TRetY NTC_TEMPs TRetRz Raw Data SSTTempTs SSTTempRZGrob TRetZs Control cabinet temperatures Phase 1 Model Development
- 60. Copyright © 2018 Softing Industrial. All rights reserved. 60 OSSA – On-Site Stream Analytics
- 61. Copyright © 2018 Softing Industrial. All rights reserved. 61
- 62. Copyright © 2018 Softing Industrial. All rights reserved. 62 Area under the ROC CurveConfusion Matrix
- 63. Copyright © 2018 Softing Industrial. All rights reserved. 63 Self-Service / Guided Analytics Benutzer: NICHT der Data Scientist -> Autonomous Analytics Sondern der Domänen-Experte
- 64. Copyright © 2018 Softing Industrial. All rights reserved. 64 ©Google,2016
- 65. Copyright © 2018 Softing Industrial. All rights reserved. 65 Machine Learning On the Edge An der Maschine In der Anlage Industrial Analytics
- 66. Copyright © 2018 Softing Industrial. All rights reserved. 66 Peter Seeberg Business Development Manager Industrial Data Intelligence Softing Industrial Automation GmbH 0151 – 1749 8159 peter.seeberg@softing.com www.industrial-data-intelligence.de/@SoftingIDI
Editor's Notes
- Und nebenbei auch noch über die Rolle von Daten und leistungsfähigen Prozessoren
- Mitgründer des Unternehmens Netscape Communications und Entwickler von Mosaic, einer der ersten international weit verbreiteten Webbrowser.
- Die Dienste der Edge App verringern signifikant das zu übertragende Datenvolumen und damit den Datenaustausch und die Übertragungsstrecke, wodurch sich die Übertragungskosten und die Wartezeiten verringern und sich die Servicequalität insgesamt verbessert. Beim Edge Computing sind zentrale Rechenzentren seltener bzw. überhaupt nicht notwendig, wodurch ein größerer Flaschenhals für den Datentransfer und eine potentielle Fehlerquelle vermieden werden. Die Sicherheit verbessert sich ebenfalls, da verschlüsselte Dateien näher am Netzwerkkern verarbeitet werden. Wenn die Daten das Unternehmen erreichen, können Viren, verfälschte Daten und Hackerangriffe frühzeitig abgefangen werden. Letztendlich erweitert die Fähigkeit zur Virtualisierung die Skalierbarkeit, was bedeutet, dass sich die Anzahl der Edge-Geräte im Netzwerk problemlos steigern lässt. Beim Edge Computing werden Echtzeit-Anforderungen im Internet der Dinge besser unterstützt als dies in der Cloud der Fall ist
- ML ist eine eigenständige Disziplin, die häufig mit KI (Künstliche Intelligenz) verwechselt wird; der Begriff KI stammt aus dem Jahre 1956 und ist damit nur geringfügig älter. Er bezeichnet den Versuch, eine menschenähnliche Intelligenz nachzubilden. Das ML kann auf diesem Weg ein erster, erfolgreicher Schritt sein, weshalb ML gerne als Teilbereich der KI verstanden wird. Doch nicht nur die Ziele dieser beiden Disziplinen sind von unter-schiedlicher Größe — es gibt einen weitaus wichtigeren Unterschied: ML ist schon da, ist bereits unter uns; wann wir das von der Künstlichen Intelligenz behaupten können, steht dagegen in den Sternen. Skip to Main Content The Keyword Latest Stories Product News Topics AI AI at Google: our principles Sundar Pichai CEO Published Jun 7, 2018 Related Articles Fighting fire with machine learning: two students use TensorFlow to predict wildfires Introducing Machine Learning Practica New York Times: Using AI to host better conversations At its heart, AI is computer programming that learns and adapts. It can’t solve every problem, but its potential to improve our lives is profound. At Google, we use AI to make products more useful—from email that’s spam-free and easier to compose, to a digital assistant you can speak to naturally, to photos that pop the fun stuff out for you to enjoy. Beyond our products, we’re using AI to help people tackle urgent problems. A pair of high school students are building AI-powered sensors to predict the risk of wildfires. Farmers are using it to monitor the health of their herds. Doctors are starting to use AI to help diagnose cancer and prevent blindness. These clear benefits are why Google invests heavily in AI research and development, and makes AI technologies widely available to others via our tools and open-source code. We recognize that such powerful technology raises equally powerful questions about its use. How AI is developed and used will have a significant impact on society for many years to come. As a leader in AI, we feel a deep responsibility to get this right. So today, we’re announcing seven principles to guide our work going forward. These are not theoretical concepts; they are concrete standards that will actively govern our research and product development and will impact our business decisions. We acknowledge that this area is dynamic and evolving, and we will approach our work with humility, a commitment to internal and external engagement, and a willingness to adapt our approach as we learn over time. Objectives for AI applications We will assess AI applications in view of the following objectives. We believe that AI should: 1. Be socially beneficial. The expanded reach of new technologies increasingly touch society as a whole. Advances in AI will have transformative impacts in a wide range of fields, including healthcare, security, energy, transportation, manufacturing, and entertainment. As we consider potential development and uses of AI technologies, we will take into account a broad range of social and economic factors, and will proceed where we believe that the overall likely benefits substantially exceed the foreseeable risks and downsides. AI also enhances our ability to understand the meaning of content at scale. We will strive to make high-quality and accurate information readily available using AI, while continuing to respect cultural, social, and legal norms in the countries where we operate. And we will continue to thoughtfully evaluate when to make our technologies available on a non-commercial basis. 2. Avoid creating or reinforcing unfair bias. AI algorithms and datasets can reflect, reinforce, or reduce unfair biases. We recognize that distinguishing fair from unfair biases is not always simple, and differs across cultures and societies. We will seek to avoid unjust impacts on people, particularly those related to sensitive characteristics such as race, ethnicity, gender, nationality, income, sexual orientation, ability, and political or religious belief. 3. Be built and tested for safety. We will continue to develop and apply strong safety and security practices to avoid unintended results that create risks of harm. We will design our AI systems to be appropriately cautious, and seek to develop them in accordance with best practices in AI safety research. In appropriate cases, we will test AI technologies in constrained environments and monitor their operation after deployment. 4. Be accountable to people. We will design AI systems that provide appropriate opportunities for feedback, relevant explanations, and appeal. Our AI technologies will be subject to appropriate human direction and control. 5. Incorporate privacy design principles. We will incorporate our privacy principles in the development and use of our AI technologies. We will give opportunity for notice and consent, encourage architectures with privacy safeguards, and provide appropriate transparency and control over the use of data. 6. Uphold high standards of scientific excellence. Technological innovation is rooted in the scientific method and a commitment to open inquiry, intellectual rigor, integrity, and collaboration. AI tools have the potential to unlock new realms of scientific research and knowledge in critical domains like biology, chemistry, medicine, and environmental sciences. We aspire to high standards of scientific excellence as we work to progress AI development. We will work with a range of stakeholders to promote thoughtful leadership in this area, drawing on scientifically rigorous and multidisciplinary approaches. And we will responsibly share AI knowledge by publishing educational materials, best practices, and research that enable more people to develop useful AI applications. 7. Be made available for uses that accord with these principles. Many technologies have multiple uses. We will work to limit potentially harmful or abusive applications. As we develop and deploy AI technologies, we will evaluate likely uses in light of the following factors: Primary purpose and use: the primary purpose and likely use of a technology and application, including how closely the solution is related to or adaptable to a harmful use Nature and uniqueness: whether we are making available technology that is unique or more generally available Scale: whether the use of this technology will have significant impact Nature of Google’s involvement: whether we are providing general-purpose tools, integrating tools for customers, or developing custom solutions AI applications we will not pursue In addition to the above objectives, we will not design or deploy AI in the following application areas: Technologies that cause or are likely to cause overall harm. Where there is a material risk of harm, we will proceed only where we believe that the benefits substantially outweigh the risks, and will incorporate appropriate safety constraints. Weapons or other technologies whose principal purpose or implementation is to cause or directly facilitate injury to people. Technologies that gather or use information for surveillance violating internationally accepted norms. Technologies whose purpose contravenes widely accepted principles of international law and human rights. We want to be clear that while we are not developing AI for use in weapons, we will continue our work with governments and the military in many other areas. These include cybersecurity, training, military recruitment, veterans’ healthcare, and search and rescue. These collaborations are important and we’ll actively look for more ways to augment the critical work of these organizations and keep service members and civilians safe. AI for the long term While this is how we’re choosing to approach AI, we understand there is room for many voices in this conversation. As AI technologies progress, we’ll work with a range of stakeholders to promote thoughtful leadership in this area, drawing on scientifically rigorous and multidisciplinary approaches. And we will continue to share what we’ve learned to improve AI technologies and practices. We believe these principles are the right foundation for our company and the future development of AI. This approach is consistent with the values laid out in our original Founders’ Letter back in 2004. There we made clear our intention to take a long-term perspective, even if it means making short-term tradeoffs. We said it then, and we believe it now.
- ML ist eine eigenständige Disziplin, die häufig mit KI (Künstliche Intelligenz) verwechselt wird; der Begriff KI stammt aus dem Jahre 1956 und ist damit nur geringfügig älter. Er bezeichnet den Versuch, eine menschenähnliche Intelligenz nachzubilden. Das ML kann auf diesem Weg ein erster, erfolgreicher Schritt sein, weshalb ML gerne als Teilbereich der KI verstanden wird. Doch nicht nur die Ziele dieser beiden Disziplinen sind von unter-schiedlicher Größe — es gibt einen weitaus wichtigeren Unterschied: ML ist schon da, ist bereits unter uns; wann wir das von der Künstlichen Intelligenz behaupten können, steht dagegen in den Sternen. Skip to Main Content The Keyword Latest Stories Product News Topics AI AI at Google: our principles Sundar Pichai CEO Published Jun 7, 2018 Related Articles Fighting fire with machine learning: two students use TensorFlow to predict wildfires Introducing Machine Learning Practica New York Times: Using AI to host better conversations At its heart, AI is computer programming that learns and adapts. It can’t solve every problem, but its potential to improve our lives is profound. At Google, we use AI to make products more useful—from email that’s spam-free and easier to compose, to a digital assistant you can speak to naturally, to photos that pop the fun stuff out for you to enjoy. Beyond our products, we’re using AI to help people tackle urgent problems. A pair of high school students are building AI-powered sensors to predict the risk of wildfires. Farmers are using it to monitor the health of their herds. Doctors are starting to use AI to help diagnose cancer and prevent blindness. These clear benefits are why Google invests heavily in AI research and development, and makes AI technologies widely available to others via our tools and open-source code. We recognize that such powerful technology raises equally powerful questions about its use. How AI is developed and used will have a significant impact on society for many years to come. As a leader in AI, we feel a deep responsibility to get this right. So today, we’re announcing seven principles to guide our work going forward. These are not theoretical concepts; they are concrete standards that will actively govern our research and product development and will impact our business decisions. We acknowledge that this area is dynamic and evolving, and we will approach our work with humility, a commitment to internal and external engagement, and a willingness to adapt our approach as we learn over time. Objectives for AI applications We will assess AI applications in view of the following objectives. We believe that AI should: 1. Be socially beneficial. The expanded reach of new technologies increasingly touch society as a whole. Advances in AI will have transformative impacts in a wide range of fields, including healthcare, security, energy, transportation, manufacturing, and entertainment. As we consider potential development and uses of AI technologies, we will take into account a broad range of social and economic factors, and will proceed where we believe that the overall likely benefits substantially exceed the foreseeable risks and downsides. AI also enhances our ability to understand the meaning of content at scale. We will strive to make high-quality and accurate information readily available using AI, while continuing to respect cultural, social, and legal norms in the countries where we operate. And we will continue to thoughtfully evaluate when to make our technologies available on a non-commercial basis. 2. Avoid creating or reinforcing unfair bias. AI algorithms and datasets can reflect, reinforce, or reduce unfair biases. We recognize that distinguishing fair from unfair biases is not always simple, and differs across cultures and societies. We will seek to avoid unjust impacts on people, particularly those related to sensitive characteristics such as race, ethnicity, gender, nationality, income, sexual orientation, ability, and political or religious belief. 3. Be built and tested for safety. We will continue to develop and apply strong safety and security practices to avoid unintended results that create risks of harm. We will design our AI systems to be appropriately cautious, and seek to develop them in accordance with best practices in AI safety research. In appropriate cases, we will test AI technologies in constrained environments and monitor their operation after deployment. 4. Be accountable to people. We will design AI systems that provide appropriate opportunities for feedback, relevant explanations, and appeal. Our AI technologies will be subject to appropriate human direction and control. 5. Incorporate privacy design principles. We will incorporate our privacy principles in the development and use of our AI technologies. We will give opportunity for notice and consent, encourage architectures with privacy safeguards, and provide appropriate transparency and control over the use of data. 6. Uphold high standards of scientific excellence. Technological innovation is rooted in the scientific method and a commitment to open inquiry, intellectual rigor, integrity, and collaboration. AI tools have the potential to unlock new realms of scientific research and knowledge in critical domains like biology, chemistry, medicine, and environmental sciences. We aspire to high standards of scientific excellence as we work to progress AI development. We will work with a range of stakeholders to promote thoughtful leadership in this area, drawing on scientifically rigorous and multidisciplinary approaches. And we will responsibly share AI knowledge by publishing educational materials, best practices, and research that enable more people to develop useful AI applications. 7. Be made available for uses that accord with these principles. Many technologies have multiple uses. We will work to limit potentially harmful or abusive applications. As we develop and deploy AI technologies, we will evaluate likely uses in light of the following factors: Primary purpose and use: the primary purpose and likely use of a technology and application, including how closely the solution is related to or adaptable to a harmful use Nature and uniqueness: whether we are making available technology that is unique or more generally available Scale: whether the use of this technology will have significant impact Nature of Google’s involvement: whether we are providing general-purpose tools, integrating tools for customers, or developing custom solutions AI applications we will not pursue In addition to the above objectives, we will not design or deploy AI in the following application areas: Technologies that cause or are likely to cause overall harm. Where there is a material risk of harm, we will proceed only where we believe that the benefits substantially outweigh the risks, and will incorporate appropriate safety constraints. Weapons or other technologies whose principal purpose or implementation is to cause or directly facilitate injury to people. Technologies that gather or use information for surveillance violating internationally accepted norms. Technologies whose purpose contravenes widely accepted principles of international law and human rights. We want to be clear that while we are not developing AI for use in weapons, we will continue our work with governments and the military in many other areas. These include cybersecurity, training, military recruitment, veterans’ healthcare, and search and rescue. These collaborations are important and we’ll actively look for more ways to augment the critical work of these organizations and keep service members and civilians safe. AI for the long term While this is how we’re choosing to approach AI, we understand there is room for many voices in this conversation. As AI technologies progress, we’ll work with a range of stakeholders to promote thoughtful leadership in this area, drawing on scientifically rigorous and multidisciplinary approaches. And we will continue to share what we’ve learned to improve AI technologies and practices. We believe these principles are the right foundation for our company and the future development of AI. This approach is consistent with the values laid out in our original Founders’ Letter back in 2004. There we made clear our intention to take a long-term perspective, even if it means making short-term tradeoffs. We said it then, and we believe it now.
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- Wir arbeiten uns fast immer mit Zeitreihen; also mit Daten die nicht kontinuierlich, sondern diskret in zeitlichen Abständen anfallen (kontinuierliche Daten können gesampled / abgetastet werden, also zu festen Zeiten ausgelesen werden.); und zwar Daten von Steuerungen, Aktoren, Sensoren aus der Fabrik, sowie eventuell aus der direkten Umgebung anfallende Daten wie Temperatur und Luftfeuchtigkeit; aus dem Grund nicht univariate, also einzelne sondern multivariate, also aus einer Mehrzahl von Zahlenwerten; meistens äquidistant, also in konstanten Abständen; Und zwar suchen wir meistens in der Zeitreihe auftretende Anomalien; Also mit Daten die sich anders verhalten als erwartet; (wie vorher erwähnt, geht es beim maschinellen Lernen um Vorhersage)
- Know-how in Soft- and Hardware
- next thing I look at now is: can we actually use a distance-measure from the clustering to find anomalies? Here is the intuition 1) take a time area of "good" operation 2) make clusters 3) score on another area 4) rate a point as outlier if is is "far away" from a known cluster 4.1. far away: I took the zScore based on the distance distribution of points assigned to a cluster as reference 2:20 here's a result: on the right side we see the scoring based on only the clusters of good operation. the background colors are: red: zScore is >4.75 (~1 out of a million), and blue, yellow are respectively 1 out of 100.000, 10.000 2:23 we can clearly see that the paper rip is in the "1 out of a million" zScore-area. So yes, for this example, we can learn a "good operation" and a zScore on the cluster distance density distribution gives us a measure of anomaly :slightly_smiling_face: 2:23 ... I again used a PCA for 3 dimensions and k-means with k=5. 2:25 One lucky coincidence I had was that the clusters are obviously "good-natured": k-Means can only handle convex / ellipsoid contours, and for the data (at least after PCA), this was the case 2:27 We also see on the right side, that there are "anomalies" even in the good operation area: These are the transitions between clusters, if we move from one cluster to another over time, we will pass the tail of probability distributions of the clusters until we enter the new state, so, yes: if the clusters represent a stable system status, then the transition is also a rare event, and from this understanding an "anomaly", but it's only a short time (the transition time)
- next thing I look at now is: can we actually use a distance-measure from the clustering to find anomalies? Here is the intuition 1) take a time area of "good" operation 2) make clusters 3) score on another area 4) rate a point as outlier if is is "far away" from a known cluster 4.1. far away: I took the zScore based on the distance distribution of points assigned to a cluster as reference 2:20 here's a result: on the right side we see the scoring based on only the clusters of good operation. the background colors are: red: zScore is >4.75 (~1 out of a million), and blue, yellow are respectively 1 out of 100.000, 10.000 2:23 we can clearly see that the paper rip is in the "1 out of a million" zScore-area. So yes, for this example, we can learn a "good operation" and a zScore on the cluster distance density distribution gives us a measure of anomaly :slightly_smiling_face: 2:23 ... I again used a PCA for 3 dimensions and k-means with k=5. 2:25 One lucky coincidence I had was that the clusters are obviously "good-natured": k-Means can only handle convex / ellipsoid contours, and for the data (at least after PCA), this was the case 2:27 We also see on the right side, that there are "anomalies" even in the good operation area: These are the transitions between clusters, if we move from one cluster to another over time, we will pass the tail of probability distributions of the clusters until we enter the new state, so, yes: if the clusters represent a stable system status, then the transition is also a rare event, and from this understanding an "anomaly", but it's only a short time (the transition time)
- Well osoa was initially meant to be an onsite offline system, with easy to use autonomous analytics. We are still working on getting the autonomous aspect better, but thats the ideaAnd yes, we use osoa as an offsite solution today, but I hope we can make a self service onsite out of itI'm totally convinced that onsite is a must if we want to position seriously as edge technology provider. Because offsite will have to compare to cloud/private cloud always. But osoa is a python in-memory system, which is made for edge
- What you see here is my first trial on density-based clustering (this time the DBSCAN), it builds cluster unsupervised based on local density it's Prinovis data (100 sensors, features extracted with sliding window, then PCA to reduce to 5 dimensions): We see 3 sensors and the two clusters (violet: gurt, green: pendelwalze, red: Leitgeschwindigkeit, then blue: clusternumer with k-means, orange: clusternumber with DBSCAN. As a comparison, the DBSCAN seams to be more precise in distiguishing clusters, it actually has build around 10 cluster, where the 5 means was told to do only 5. For the DBSCAN, there is no such setting, it finds the clusters by itself Now what am I after with this? The DBSCAN look similar to k-means results, right? Well, the densitiy-based clustering can do anomaly detection in the scoring phase. The "density of values" will not be very high for anomaly value. k-Means instead can't do anomaly in scoring, unless you abuse k-Means by building in e.g. a limit based on previous data Another important thing to mention: the distance metric for k-Means is typically euclidian, that means we cannot support concave structures. Density-based approaches can support any shape of data clusters.
- here is another nice visualization of the k-Means capabilities, I used raw data with PCA to 3 dimensions (for a nice 3d-plot) and then a k-Means(k=5) for clustering: on the right side, we see the time series with the background colors of the clusters, on the left we see the pca-vectors in a 3d 11:14 What is interesting is to see that the paperrips (dark blue, green clusters) are clearly separated from the "normal operation" like the light blue and pink
- The ROC curve shows classification performance as a trade off between selectivity and sensitivity at different detection threshold levels. It is useful in the selection of the optimal threshold as well as for choosing between competing models. Its usefulness is limited in the case of imbalanced classes, because it only focuses on the positive instances. In such scenarios, other measures are preferred (such as precision-recall AUC). *The model evaluation metrics depend on the type of model used as well as the characteristics of the dataset used (balanced vs imbalanced, binary vs multi-class classification, costs assigned to misclassifications of certain classes etc.)