[Research] deploying predictive models with the actor framework - Brian GawaltPAPIs.io
Build a better, faster, more efficient predictive API with the Actor model of programming. Latency, logging, full utilization are all easily handled with this framework. Upwork (formerly Elance-oDesk) freelancer availability model — anticipating who's looking for work right now — is now a real-time service, without costly or complicated build-out of our stack or our datacenter, thanks to the Actor model.
In this presentation given at TheIJC USA in Chicago, Meteor's Jonathan Wilson explores the challenges we face with regard to inkjet printing: Inkjet is no stranger to complex engineering, from printheads to ink delivery systems and drive electronics. The growing demand for high speed, single pass industrial inkjet systems in a myriad of applications presents ever more tricky challenges including cross web calibration, mottling and nozzle out detection/compensation. Tackling these issues requires a multi-disciplinary approach, uniting skills from hardware, software and color management to successfully exceed reliability and print consistency requirements.
Predictive Maintenance of Ball Bearing using Digital TwinBarathkumar109
This complete presentation will reveal my work on predictive maintenance on component level simulation and prediction through simulation of Digital twin. Due to the paradigm shift in the manufacturing and aviation maintenance fields to preempt the future and growth potential in digital maintenance.
Doubts pls connect me through linkedin:www.linkedin.com/in/barathkumar861998
mailID:barathkumar412@outlook.com
[Research] deploying predictive models with the actor framework - Brian GawaltPAPIs.io
Build a better, faster, more efficient predictive API with the Actor model of programming. Latency, logging, full utilization are all easily handled with this framework. Upwork (formerly Elance-oDesk) freelancer availability model — anticipating who's looking for work right now — is now a real-time service, without costly or complicated build-out of our stack or our datacenter, thanks to the Actor model.
In this presentation given at TheIJC USA in Chicago, Meteor's Jonathan Wilson explores the challenges we face with regard to inkjet printing: Inkjet is no stranger to complex engineering, from printheads to ink delivery systems and drive electronics. The growing demand for high speed, single pass industrial inkjet systems in a myriad of applications presents ever more tricky challenges including cross web calibration, mottling and nozzle out detection/compensation. Tackling these issues requires a multi-disciplinary approach, uniting skills from hardware, software and color management to successfully exceed reliability and print consistency requirements.
Predictive Maintenance of Ball Bearing using Digital TwinBarathkumar109
This complete presentation will reveal my work on predictive maintenance on component level simulation and prediction through simulation of Digital twin. Due to the paradigm shift in the manufacturing and aviation maintenance fields to preempt the future and growth potential in digital maintenance.
Doubts pls connect me through linkedin:www.linkedin.com/in/barathkumar861998
mailID:barathkumar412@outlook.com
To view recording of this webinar please use below URL:
http://wso2.com/library/webinars/2016/05/making-smarter-systems-with-iot-and-analytics/
Many systems today play an increasingly important role in our lives and communities. Systems can learn and adopt by themselves without having to follow a structured, predefined execution flow. They are digitally independant and have become smarter, faster and more reliable. Digital intelligence can be embedded not just in individual components but also across entire systems, impacting everything from traffic flows and electric power to the way our food is grown, processed and delivered. This is achieved by employing the capabilities of multiple disciplines. Devices and systems produce large volume unstructured data. Real-time or historical data can be analyzed to uncover hidden patterns, correlations and other insights and this information is then fed into machine learning algorithms that calculates predictions.
WSO2’s analytics platform together with the WSO2 IoT Server can provide all these capabilities. This webinar aims to
Identify key capabilities needed when composing a smart system
Explore how WSO2’s analytics platform can be used to make a system smarter
Discuss how WSO2 IoT Server manages and enable devices
MIT Enterprise Forum of Cambridge Connected Things 2017 panel discussion on "IoT Analytics: Using Analytics to Generate High Value from IoT in the Real World"
AWS re:Invent 2016: Understanding IoT Data: How to Leverage Amazon Kinesis in...Amazon Web Services
The growing popularity and breadth of use cases for IoT are challenging the traditional thinking of how data is acquired, processed, and analyzed to quickly gain insights and act promptly. Today, the potential of this data remains largely untapped. In this session, we explore architecture patterns for building comprehensive IoT analytics solutions using AWS big data services. We walk through two production-ready implementations. First, we present an end-to-end solution using AWS IoT, Amazon Kinesis, and AWS Lambda. Next, Hello discusses their consumer IoT solution built on top of Amazon Kinesis, Amazon DynamoDB, and Amazon Redshift.
Data Analytics in your IoT SolutionFukiat Julnual, Technical Evangelist, Mic...BAINIDA
Data Analytics in your IoT SolutionFukiat Julnual, Technical Evangelist, Microsoft (Thailand) Limited ในงาน THE FIRST NIDA BUSINESS ANALYTICS AND DATA SCIENCES CONTEST/CONFERENCE จัดโดย คณะสถิติประยุกต์และ DATA SCIENCES THAILAND
Talks by Carmelo Floridia, Senior Engineer (Data Processing) at BaxEnergy, at the Big Data for You event "Recommendation Systems: Talks & Workshop" (Catania, Feb. 25th, 2017).
Carmelo offers a panorama view of BaxEnergy activities, in particular about Solar power plants, on how they analyse power curves and on how they can provide a real-time monitoring service and forecasting.
Carmelo also describes the primary characteristics of Big Data and Machine Learning, and mention the Microsoft Azure Technology as the one currently used at Bax Energy.
Although one can't see this from the slides, Carmelo gave us a live demonstration of Human model learning (based on kinect)!
Analyzing data and driving business decisions to the edge of Internet-of-Things (IoT) is rapidly becoming critical for any IoT solution. And for real-time analysis of the data as it streams in is vital to many business processes. Informix, as the data management system of choice for IoT solutions delivers significant value proposition for businesses across all industry segments looking to deploy IoT Solutions. And with Apache Edgent/Quarks integration, you get real-time analysis of streaming IoT data.
[Tutorial] building machine learning models for predictive maintenance applic...PAPIs.io
This talk introduces the landscape and challenges of predictive maintenance applications in the industry, illustrates how to formulate (data labeling and feature engineering) the problem with three machine learning models (regression, binary classification, multi-class classification) using a publicly available aircraft engine run-to-failure data set, and showcases how the models can be conveniently trained and compared with different algorithms in Azure ML.
Embedded subscriber database analytics help operators improve internal efficiency and monetize data assets, while exploring new cross-vertical Internet of Things (IoT) applications.
The (R)evolution of Predictive Operations & MaintenanceCapgemini
Effectively leveraging Predictive Analytics will likely be one of the most (r)evolutionary trends in operations and maintenance. OEM’s, commercial and military operators, and MRO organizations are exploring the technologies instrumental in taking operations and maintenance to the next level.
Hear from industry leaders on the latest leading practices and state-of-the-art technologies to gain a view of the road ahead.
MONETIZABLE VALUE CREATION WITH INDUSTRIAL IoTugkaz
Part -1 Of Whitepaper Series by ArcInsight Partners (IIoT Analysts & Strategy Advisors)
ArcInsight Partners Is A Global Research & Strategy Advisory Group With A Focus On Industrial Internet Of Things (IIoT), Enterprise Digital Transformation & Smart City Evolutionary Strategies. Based in San Francisco, the firm operates from multiple locations.
Contact: Ug Kazimori (ugetsukazimori@gmail.com)
IoT-Daten: Mehr und schneller ist nicht automatisch besser.
Über optimale Sampling-Strategien, wie man rechnen kann, ob IoT sich rechnet, und warum es nicht immer Deep Learning und Real-Time-Analytics sein muss. (Folien Deutsch/Englisch)
Mehr und schneller ist nicht automatisch besser - data2day, 06.10.16Boris Adryan
Das Gesetz der großen Zahlen gilt immer: Die statistische Sicherheit nimmt mit der Anzahl der Datenpunkte immer zu, sofern die Datennahme fair erfolgt. Leider kostet das Sammeln der Daten oftmals Geld, und so ist man vor allem im Bereich der Sensorik (Stichwort: Internet der Dinge) gezwungen, sinnvolle Kompromisse einzugehen. In diesem Vortrag fasse ich die Erkenntnisse eines Projekts zusammen, in dem die Datenanalytik zeigte, dass man zukünftig nur 60% der ausgebrachten Sensoren wirklich braucht. Auch muss es nicht immer Echtzeit-Analyse sein: Mit einer auf den Business-Case abgestimmten Datenstrategie lassen sich unnötige Ausgaben vermeiden.
To view recording of this webinar please use below URL:
http://wso2.com/library/webinars/2016/05/making-smarter-systems-with-iot-and-analytics/
Many systems today play an increasingly important role in our lives and communities. Systems can learn and adopt by themselves without having to follow a structured, predefined execution flow. They are digitally independant and have become smarter, faster and more reliable. Digital intelligence can be embedded not just in individual components but also across entire systems, impacting everything from traffic flows and electric power to the way our food is grown, processed and delivered. This is achieved by employing the capabilities of multiple disciplines. Devices and systems produce large volume unstructured data. Real-time or historical data can be analyzed to uncover hidden patterns, correlations and other insights and this information is then fed into machine learning algorithms that calculates predictions.
WSO2’s analytics platform together with the WSO2 IoT Server can provide all these capabilities. This webinar aims to
Identify key capabilities needed when composing a smart system
Explore how WSO2’s analytics platform can be used to make a system smarter
Discuss how WSO2 IoT Server manages and enable devices
MIT Enterprise Forum of Cambridge Connected Things 2017 panel discussion on "IoT Analytics: Using Analytics to Generate High Value from IoT in the Real World"
AWS re:Invent 2016: Understanding IoT Data: How to Leverage Amazon Kinesis in...Amazon Web Services
The growing popularity and breadth of use cases for IoT are challenging the traditional thinking of how data is acquired, processed, and analyzed to quickly gain insights and act promptly. Today, the potential of this data remains largely untapped. In this session, we explore architecture patterns for building comprehensive IoT analytics solutions using AWS big data services. We walk through two production-ready implementations. First, we present an end-to-end solution using AWS IoT, Amazon Kinesis, and AWS Lambda. Next, Hello discusses their consumer IoT solution built on top of Amazon Kinesis, Amazon DynamoDB, and Amazon Redshift.
Data Analytics in your IoT SolutionFukiat Julnual, Technical Evangelist, Mic...BAINIDA
Data Analytics in your IoT SolutionFukiat Julnual, Technical Evangelist, Microsoft (Thailand) Limited ในงาน THE FIRST NIDA BUSINESS ANALYTICS AND DATA SCIENCES CONTEST/CONFERENCE จัดโดย คณะสถิติประยุกต์และ DATA SCIENCES THAILAND
Talks by Carmelo Floridia, Senior Engineer (Data Processing) at BaxEnergy, at the Big Data for You event "Recommendation Systems: Talks & Workshop" (Catania, Feb. 25th, 2017).
Carmelo offers a panorama view of BaxEnergy activities, in particular about Solar power plants, on how they analyse power curves and on how they can provide a real-time monitoring service and forecasting.
Carmelo also describes the primary characteristics of Big Data and Machine Learning, and mention the Microsoft Azure Technology as the one currently used at Bax Energy.
Although one can't see this from the slides, Carmelo gave us a live demonstration of Human model learning (based on kinect)!
Analyzing data and driving business decisions to the edge of Internet-of-Things (IoT) is rapidly becoming critical for any IoT solution. And for real-time analysis of the data as it streams in is vital to many business processes. Informix, as the data management system of choice for IoT solutions delivers significant value proposition for businesses across all industry segments looking to deploy IoT Solutions. And with Apache Edgent/Quarks integration, you get real-time analysis of streaming IoT data.
[Tutorial] building machine learning models for predictive maintenance applic...PAPIs.io
This talk introduces the landscape and challenges of predictive maintenance applications in the industry, illustrates how to formulate (data labeling and feature engineering) the problem with three machine learning models (regression, binary classification, multi-class classification) using a publicly available aircraft engine run-to-failure data set, and showcases how the models can be conveniently trained and compared with different algorithms in Azure ML.
Embedded subscriber database analytics help operators improve internal efficiency and monetize data assets, while exploring new cross-vertical Internet of Things (IoT) applications.
The (R)evolution of Predictive Operations & MaintenanceCapgemini
Effectively leveraging Predictive Analytics will likely be one of the most (r)evolutionary trends in operations and maintenance. OEM’s, commercial and military operators, and MRO organizations are exploring the technologies instrumental in taking operations and maintenance to the next level.
Hear from industry leaders on the latest leading practices and state-of-the-art technologies to gain a view of the road ahead.
MONETIZABLE VALUE CREATION WITH INDUSTRIAL IoTugkaz
Part -1 Of Whitepaper Series by ArcInsight Partners (IIoT Analysts & Strategy Advisors)
ArcInsight Partners Is A Global Research & Strategy Advisory Group With A Focus On Industrial Internet Of Things (IIoT), Enterprise Digital Transformation & Smart City Evolutionary Strategies. Based in San Francisco, the firm operates from multiple locations.
Contact: Ug Kazimori (ugetsukazimori@gmail.com)
IoT-Daten: Mehr und schneller ist nicht automatisch besser.
Über optimale Sampling-Strategien, wie man rechnen kann, ob IoT sich rechnet, und warum es nicht immer Deep Learning und Real-Time-Analytics sein muss. (Folien Deutsch/Englisch)
Mehr und schneller ist nicht automatisch besser - data2day, 06.10.16Boris Adryan
Das Gesetz der großen Zahlen gilt immer: Die statistische Sicherheit nimmt mit der Anzahl der Datenpunkte immer zu, sofern die Datennahme fair erfolgt. Leider kostet das Sammeln der Daten oftmals Geld, und so ist man vor allem im Bereich der Sensorik (Stichwort: Internet der Dinge) gezwungen, sinnvolle Kompromisse einzugehen. In diesem Vortrag fasse ich die Erkenntnisse eines Projekts zusammen, in dem die Datenanalytik zeigte, dass man zukünftig nur 60% der ausgebrachten Sensoren wirklich braucht. Auch muss es nicht immer Echtzeit-Analyse sein: Mit einer auf den Business-Case abgestimmten Datenstrategie lassen sich unnötige Ausgaben vermeiden.
Making Actionable Decisions at the Network's EdgeCognizant
With the vast analytical power unleashed by the Internet of Things (IoT) ecosystem, IT organizations must be able to apply both cloud analytics and edge analytics - cloud for strategic decision-making and edge for more instantaneous response based on local sensors and other technology.
Just because you can doesn't mean that you should - thingmonk 2016Boris Adryan
Big data! Fast data! Real-time analytics! These are buzzwords commonly associated with platform offerings around IoT.
Although the Law of large numbers always applies, just because you can deploy more sensors doesn't automatically mean that you should. After all, they cost money, bandwidth, and can be a pain to maintain. On the example of the Westminster Parking Trial, I'd like to show how analytics on preliminary survey data could have reduced the number of deployed sensors significantly.
A similar logic goes for fast and real-time analytics. While being advertised as killer features, many people new to IoT and analytics are not even aware that they might get away with batch processing. On the example of flying a drone, I'd like to discuss for which use cases I'd apply edge processing (on the drone), stream or micro-batch analytics (when data arrives at the platform) or work on batched data (stored in a database).
Future-Proofing Your Business with TechnologySkoda Minotti
Technology is rapidly moving from a business enabler to the core of the business. New technologies such as “big data” and analytics, the internet of things (IoT), robotics, mobile technology, artificial intelligence and cybersecurity are transforming the way business gets done.
We explore the business implications of technology and their impact on businesses of all sizes and scopes, and presents strategies for charting a path through these disruptive times.
Michael will discuss some of the issues and challenges around Big Data. It is all very well building Big Data friendly databases to manage the tidal wave of real-time data that the IoT inevitably creates but this must also be incorporated into legacy data to deliver actionable insight.
Proof of concepts and use cases with IoT technologiesHeikki Ailisto
Set of proof of concept and use cases with internet of things technologies are presented with one sliders. In each case, the IoT challenge, result, benefits and use case example are given.
An emulation framework for IoT, Fog, and Edge ApplicationsMoysisSymeonides
In this talk, we presented an emulation framework that eases the modeling, deployment, and large-scale experimentation of fog and 5G testbeds. The framework provides a toolset to (i) model complex fog topologies comprised of heterogeneous resources, network capabilities, and QoS criteria; (ii) abstractions for physical 5G infrastructure concepts such as radio units, edge servers, mobile nodes, user equipment, and node trajectories; (iii) deploy the modeled configuration and services using popular containerised descriptions to a cloud or
local environment; (iv) experiment, measure and evaluate the deployment by injecting faults, adapting the configuration at runtime, real-time updates of the radio network (i.e., signal strength) and respective network QoS to test different “what-if” scenarios that reveal the limitations of service before introduced to the public. The framework has been used for studying the performance of Intelligent transportation services, Industrial IoT micro-service applications, geo-distributed deployments of big data engines, and many more.
The presentation took place at Athens Demokritos Research Center organised by SKEL | The AI Lab
video: https://www.youtube.com/watch?v=z37I1QVFabg
Fin fest 2014 - Internet of Things and APIsRobert Greiner
An overview of the core concepts behind the ultra-hyped Internet of Things. We start the presentation with an overview and slight re-classification of what the Internet of Things is. Then, we jump into how to *serve* the internet of things - discussing a homebrew project using the RaspberryPi and Microsoft Azure.
Data Summer Conf 2018, “Architecting IoT system with Machine Learning (ENG)” ...Provectus
In this presentation, the speaker will share his experiences from building successful IoT systems. He will also explain why many IoT systems fail to get traction and how Machine Learning can help in that. Finally, he will talk about the right system architecture and touch upon some of the ML algorithms for IoT systems.
CTO of ParStream Joerg Bienert hold a presentation on February 25, 2014 about Big Data for Business Users. He talked about several use cases of current ParStream customers and ParStreams' technology itself.
Fog Computing Reality Check: Real World Applications and ArchitecturesBiren Gandhi
Is Fog Computing just a buzz or a real business?
The IoT is flooded with a variety of platforms and solutions. Fog Computing has been notably appearing as an evolving term in the context of IoT software. There is skepticism that Fog Computing is just another buzzword destined to disappear in the dust of time. Get insight from concrete business cases in a variety of IoT verticals – Agriculture, Industrial Manufacturing, Transportation, Smart & Connected Communities etc. and learn how Fog Computing can play a substantial role in each one of these verticals. Develop a judicious point of view with respect to the future of Fog Computing through market research, technology disruption vectors and ROI use cases presented in this session.
Building a reliable and scalable IoT platform with MongoDB and HiveMQDominik Obermaier
Today’s Internet of Things (IoT) is enabling companies to blend together the physical and digital worlds, creating new business models and generating insights that increase productivity at once unimaginable levels. However, managing the ever growing volume of heterogeneous IoT data from disparate devices, systems and applications both on premise and in the cloud can be a challenging endeavour without a scalable and reliable IoT platform.
In this webinar, we will explore why and how companies are leveraging HiveMQ and MongoDB to build exactly that: a scalable and reliable IoT platform. Based upon a sample fleet management scenario, we will explain how telematics data can be routed via MQTT and efficiently stored to provide analytics and insights into the data.
Key Learnings
- Common challenges and pitfalls of IoT projects
- Required components for effectively handling data with an IoT platform
- HiveMQ for MQTT to enable bi-directional device communication over unstable networks
- MongoDB as the flexible and scalable modern data platform combining data from different sources and powering your applications
- Why MongoDB and HiveMQ is such a great combination
Explore the innovative world of trenchless pipe repair with our comprehensive guide, "The Benefits and Techniques of Trenchless Pipe Repair." This document delves into the modern methods of repairing underground pipes without the need for extensive excavation, highlighting the numerous advantages and the latest techniques used in the industry.
Learn about the cost savings, reduced environmental impact, and minimal disruption associated with trenchless technology. Discover detailed explanations of popular techniques such as pipe bursting, cured-in-place pipe (CIPP) lining, and directional drilling. Understand how these methods can be applied to various types of infrastructure, from residential plumbing to large-scale municipal systems.
Ideal for homeowners, contractors, engineers, and anyone interested in modern plumbing solutions, this guide provides valuable insights into why trenchless pipe repair is becoming the preferred choice for pipe rehabilitation. Stay informed about the latest advancements and best practices in the field.
Cosmetic shop management system project report.pdfKamal Acharya
Buying new cosmetic products is difficult. It can even be scary for those who have sensitive skin and are prone to skin trouble. The information needed to alleviate this problem is on the back of each product, but it's thought to interpret those ingredient lists unless you have a background in chemistry.
Instead of buying and hoping for the best, we can use data science to help us predict which products may be good fits for us. It includes various function programs to do the above mentioned tasks.
Data file handling has been effectively used in the program.
The automated cosmetic shop management system should deal with the automation of general workflow and administration process of the shop. The main processes of the system focus on customer's request where the system is able to search the most appropriate products and deliver it to the customers. It should help the employees to quickly identify the list of cosmetic product that have reached the minimum quantity and also keep a track of expired date for each cosmetic product. It should help the employees to find the rack number in which the product is placed.It is also Faster and more efficient way.
Immunizing Image Classifiers Against Localized Adversary Attacksgerogepatton
This paper addresses the vulnerability of deep learning models, particularly convolutional neural networks
(CNN)s, to adversarial attacks and presents a proactive training technique designed to counter them. We
introduce a novel volumization algorithm, which transforms 2D images into 3D volumetric representations.
When combined with 3D convolution and deep curriculum learning optimization (CLO), itsignificantly improves
the immunity of models against localized universal attacks by up to 40%. We evaluate our proposed approach
using contemporary CNN architectures and the modified Canadian Institute for Advanced Research (CIFAR-10
and CIFAR-100) and ImageNet Large Scale Visual Recognition Challenge (ILSVRC12) datasets, showcasing
accuracy improvements over previous techniques. The results indicate that the combination of the volumetric
input and curriculum learning holds significant promise for mitigating adversarial attacks without necessitating
adversary training.
Welcome to WIPAC Monthly the magazine brought to you by the LinkedIn Group Water Industry Process Automation & Control.
In this month's edition, along with this month's industry news to celebrate the 13 years since the group was created we have articles including
A case study of the used of Advanced Process Control at the Wastewater Treatment works at Lleida in Spain
A look back on an article on smart wastewater networks in order to see how the industry has measured up in the interim around the adoption of Digital Transformation in the Water Industry.
Hybrid optimization of pumped hydro system and solar- Engr. Abdul-Azeez.pdffxintegritypublishin
Advancements in technology unveil a myriad of electrical and electronic breakthroughs geared towards efficiently harnessing limited resources to meet human energy demands. The optimization of hybrid solar PV panels and pumped hydro energy supply systems plays a pivotal role in utilizing natural resources effectively. This initiative not only benefits humanity but also fosters environmental sustainability. The study investigated the design optimization of these hybrid systems, focusing on understanding solar radiation patterns, identifying geographical influences on solar radiation, formulating a mathematical model for system optimization, and determining the optimal configuration of PV panels and pumped hydro storage. Through a comparative analysis approach and eight weeks of data collection, the study addressed key research questions related to solar radiation patterns and optimal system design. The findings highlighted regions with heightened solar radiation levels, showcasing substantial potential for power generation and emphasizing the system's efficiency. Optimizing system design significantly boosted power generation, promoted renewable energy utilization, and enhanced energy storage capacity. The study underscored the benefits of optimizing hybrid solar PV panels and pumped hydro energy supply systems for sustainable energy usage. Optimizing the design of solar PV panels and pumped hydro energy supply systems as examined across diverse climatic conditions in a developing country, not only enhances power generation but also improves the integration of renewable energy sources and boosts energy storage capacities, particularly beneficial for less economically prosperous regions. Additionally, the study provides valuable insights for advancing energy research in economically viable areas. Recommendations included conducting site-specific assessments, utilizing advanced modeling tools, implementing regular maintenance protocols, and enhancing communication among system components.
Sachpazis:Terzaghi Bearing Capacity Estimation in simple terms with Calculati...Dr.Costas Sachpazis
Terzaghi's soil bearing capacity theory, developed by Karl Terzaghi, is a fundamental principle in geotechnical engineering used to determine the bearing capacity of shallow foundations. This theory provides a method to calculate the ultimate bearing capacity of soil, which is the maximum load per unit area that the soil can support without undergoing shear failure. The Calculation HTML Code included.
Sachpazis:Terzaghi Bearing Capacity Estimation in simple terms with Calculati...
Global C4IR-1 Masterclass Adryan - Zuehlke Engineering 2017
1. IoT analytics: There’s not
just predictive maintenance
Dr. Boris Adryan
Head of IoT & Data Analytics
Zühlke Engineering GmbH
@BorisAdryan
Presented at Consortium for the 4th Revolution Executive Briefing Day (C4IR-1
Cambridge, UK 2-3 February 2017 www.cir-strategy.com/events
2. Zühlke: Empowering Ideas
Business Innovation - from idea to market success
founded in 1968
> 8.000 projects
800 employees
120 million EUR turnover (2015)
key verticals:
manufacturing, systems engineering
medical & pharma
financial sector
consumer products
The Internet of Things
is a key ingredient to merge the digital
and the real world to provide novel
business opportunities.
Your partner for business innovation
Zühlke Engineering unites business &
technological competence: digital
solutions for a connected world.
6. Predictive maintenance
Case study: Drill bit of a milling machine
Image source:
Wikipedia
• industrial drilling is highly automated
(CNC)
• the drill bit is an expensive
consumable
• changing the drill bit too late can
• impinge on product quality
• destroy the product
• destroy the machine
7. often: condition-based replacement
Maintenance strategy
not considering remaining useful lifetime
often, the “condition” can only be guessed
best approximation: time in use
based on statistical considerations
(still a guess, but it’s educated!)
predictive!
11. data recording model building test use in production
data recording
(production system)
evaluation
raw data clean-up
feature
engineering
model
learning
model
selection
labour intense compute intensebrain intense
Machine learning pipeline
development
production
13. distributed local experimental
pipeline complex simple simple
model building hit-or-miss hit-or-miss simple
model update complex simple simple
production system “lab”
Learning on development vs
production system
data
resources
proddev
14. Edge, fog and cloud computing
Edge
Pro:
- immediate compression from raw
data to actionable information
- cuts down traffic
- fast response
Con:
- loses potentially valuable raw data
- developing analytics on embedded
systems requires specialists
- compute costs valuable battery life
Cloud
Pro:
- compute power
- scalability
- familiarity for developers
- integration centre across
all data sources
- cheapest ‘real-time’
option
Con:
- traffic
Fog
Pro:
- same as Edge
- closer to ‘normal’ development work
- gateways often mains-powered
Con:
- loses potentially valuable raw data
16. Analytical response times for IoT
microseconds
to seconds
seconds to
minutes
minutes
to hours
hours to
weeks
on
device
on
stream
in batch
am I falling?
counteract
battery level
should I land?
how many
times did I
stall?
what’s the best
weather for
flying?
in process
in database
operational insight
performance insight
strategic insight
e.g. Kalman filter
e.g. with machine learning
e.g. rules engine
e.g. summary stats
17. Be as fast as you must.
But don’t be any faster
just for the sake of it.
Summary: IoT Data Analytics (I)
18. Data analytics can be a
deal sweetener!
39% of survey participants
are worried about the
upfront investment for an
industrial IoT solution.
CASE 1: Smart Parking
21. Can we learn an optimal
deployment and sampling pattern?
•sampling rate of 5-10 min
•data over 2 weeks in May 2015
•overall 2.6 million data points
Can we make the customer’s budget go further by
• reducing the number of sensors in a geographic area?
• lowering the sampling rate for better battery life?
22. Good news: temporal occupancy
pattern roughly predicts neighbours
lots in Southampton
lots around
the corner of
each other
750 parking lots
23. A caveat: Is a high-degree of correlation
a function of parking lot size?
finding two lots of 20
spaces that correlate
finding two lots of 3
spaces that correlate
0:00 12:00 23:59
0:00 12:00 23:59
“more likely”
“less likely”
24. Bootstrapping in DBSCAN clusters
Simulation: Swap the occupancy vectors between parking
lots of similar size and test per grid cell if these lots still
correlate
25. Stratification strategy
3 lots with cc > 0.5
2 spaces
4 spaces
4 spaces
Test:
1. Take occupancy profile of
ONE random 2-space parking
lot and TWO random 4-space
parking lots.
2. Determine cc.
3. Repeat n times and get a cc
distribution for that parking lot
combination.
27. Even a temporary survey would have allowed us to make
a recommendation: 60% of the sensors at half the time
are effectively sufficient for the use case.
Summary: IoT Data Analytics (II)
28. Data analytics can be a
deal sweetener!
39% of survey participants
are worried about the
upfront investment for an
industrial IoT solution.
CASE 2: Asset Tracking
29. IoT - is it worth it?
The upgrade of a ‘dumb’ asset to
a ‘smart’ asset is an investment.
time,
money
31. Data sources
Let’s assume the future isn’t going to be
much different than the past…
• log from past site visits: approx. likelihood for maintenance
• a collection of traffic data that’s somewhat representative
33. Maintenance likelihood
• test for dependency
between Monday and
Wednesday tours
none
• test for dependency
within tours
none
The assumption of temporal
uniformity is reasonable.
34. Monte Carlo simulations
p1(need today)
patterns for a
demand-driven tour
‘cost function’:
sum of edges
base
default tour
base
p2(need today)
p3(need today)
p4(need today)
p5(need today)
p6(need today)
35. Travelling salesman problem
what’s the most
reasonable tour
from to ,
visiting all ?
heuristic search
is good enough,
but requires a
distance matrix
36. Traffic harvesting
• based on Google API
• generate a distribution
of travel times for each
edge in the graph,
dependent on time of
day (weekdays only)
37. IoT - is it worth it?
cost
awaiting
confirmation!
weeks
cost
weeks
38. Preliminary data taken from manual surveys, along with
‘open data’ and other sources can help making an
educated guess of the business value of an IoT solution.
Summary: IoT Data Analytics (III)
39. Dr. Boris Adryan
eMail: boad@zuehlke.com
Twitter: @BorisAdryan
www.linkedin.com/in/
borisadryan
Thank you!