Chương 2 - CPS.pdf

Ho Chi Minh City
University of Technology
Industry 4.0 technologies in Mechanical Engineering
PhD. Quang-Phuoc Tran
Course : Industry 4.0 Technologies in Mechanical Engineering
Lecturer : PhD Tran Quang Phuoc

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Content
Chapter 2: Cyber physical system

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Cyber physical system concepts
+ Cyber-physical systems (CPSs) incorporate the close interactions
between computing resources (such as computer hardware and software)
and physical objects (such as a car, human body, etc.).
+ A CPS typically includes a network of devices that receive and perform
physical actions while simultaneously being controlled and monitored by
computational and communication software
+ Computational and digital technologies will soon be found in, and play
an integral role in, many physical structures and devices
+ CPSs also have the same potential to change how we interact with the
physical world around us. The implementation of the Internet is based on
the integration of major advancements in network technology, applications,
and infrastructure. Likewise, CPSs can be seen as the integration of
embedded systems, sensors, and control systems.
+ CPS software system design must include a diverse pool of specific application tools that can automatically and
independently perform a variety of functions according to the specific constraints of the system’s hardware capabilities and
application requirements

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Cyber physical system concepts

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Cyber physical system characteristics
1.Physical system: This includes physical system design such as hardware design, energy
management, hardware size and connectivity encapsulation and system testing. Engineers
and scientists in this field have a deep understanding of mechanics, electronics, biology and
chemistry; they understand the technical characteristics of sensors, and they know how to
process measurement data using signal processing technology. Every physical system has its
own network characteristics, a maximized multilevel network coverage, a variety of complex
temporal and spatial scales to meet the time requirements of different tasks and a high degree
of automation.
2. Information system: The information in physical system engineering can be transformed
into the rules and models of a software system. The most basic task is to reach a balance
among factors such as real-time system, network system, file system, hierarchical storage
system, memory management, modular software design, concurrent design and formal
verification.

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3. Integration of heterogeneous systems: CPS are heterogeneous distributed systems
featuring the integration and interaction of information systems and physical systems.
4. Security, real-time capability and predictability: Because network systems and physical
system are open, invasions, tampering, counterfeiting and other malicious attacks may occur,
as well as delays in network transmission.
Cyber-physical system must be able to offer credibility, security, validity, real-time capability,
dynamism and predictability.
+ For credibility, the identity of information-collecting sources or control instruction senders
must be authenticated, and the receiver must be able to determine the real identity of the
sender to prevent counterfeiting.
+ Security requires the encryption and decryption of sent or received information, and the
privacy of information must be protected.

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+For validity, the accuracy of processing, as well as the validity and integrity of information
or instructions sent, must be guaranteed to prevent uncertainties and noise in CPS processing
from affecting the system’s processing accuracy.
+ Real time capability means collected information or instructions must be transmitted in a
timely manner to meet the real-time requirements of task processing.
+ Dynamism includes dynamic reorganization and reconfiguration, automatically adjusting
rules and generating commands based on the task requirements; it also includes changes in
external environments to eliminate bias and meet task requirements according to preset
rules.
+With predictability, CPS resource allocation strategy can reasonably allocate resources to
multiple competing real-time tasks at any moment and in any case, so that the real-time
requirements of every real-time task can be satisfied.

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CPS Architecture
The CPS system structure can be divided into
three layers: user, information system and physical
system layers. The physical system is the
foundation of the CPS; it is composed of
embedded systems, sensor networks, smart chips
and so on, and it takes charge of the collection and
transmission of information and the execution of
control signals. The information system layer is
mainly responsible for the transmission and
processing of the data collected by the physical
system. The user layer completes the work,
including data query, strategy and safety
protection, in a human–computer interaction
environment. Cyber-physical system runs as a
closed loop.

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Virtual Reality System

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Simplified layout of a CPS

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Service-oriented architecture of a CPS

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The so-called 5-level CPS structure,
or the 5C architecture, provides step-
by-step guidelines for developing and
deploying a CPS for manufacturing
applications. In general, CPS has two
main functional components:
1. Advanced connectivity ensures
real-time data acquisition from the
physical world and information
feedback from cyberspace;
2. Intelligent data management,
analytics and computational
capability construct cyberspace.
CPS 5C Level Architecture

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CPS 5C Level Architecture

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Acquiring accurate and reliable data from machines and their
components is the first step in developing a CPS application. The
data might be directly measured by sensors or obtained from
controllers or enterprise manufacturing systems, such as:
+ Enterprise resource planning (ERP)
+ Manufacturing execution system (MES)
+ Supply chain management (SCM)
+ Collaborative manufacturing management (CMM).
Smart Connection

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- Two important factors at this level must
be considered:
+ First, considering various types of data, a
seamless and tether-free method to manage
data acquisition procedure and transferring
data to the central server is required where
specific protocols such as MTConnect are
useful.
+ Second, proper sensors (type and
specification) must be selected for the first
level.

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Case study
Schematic of process planning, machining, and data capture used in the case study

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Integrated manufacturing process planning verification

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Data-to-information conversion
Meaningful information has to be
inferred from the data. Currently, there
are several tools and methodologies
available for the data to information
conversion level. In recent years,
extensive focus has been applied to
develop these algorithms specifically
for prognostics and health management
applications. By calculating health
value, estimated remaining useful life
and etc., the second level of CPS
architecture brings self-awareness to
machines

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The cyber level is a central information hub, with information pushed
to it from every connected machine. Massive amounts of information
are gathered; thus, specific analytics have to be used to extract
relevant information on the status of individual machines. With these
analytics, the performance of a single machine can be compared
with others in the fleet and rated accordingly. In addition, similarities
between machine performance and the performance of previous
assets (historical information) can be measured to predict future
behavior.
Cyber

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Implementing CPS upon this level generates a thorough knowledge of the
monitored system. Proper presentation of the acquired knowledge to
expert users supports the correct decision to be taken. Since comparative
information as well as individual machine status is available, decision on
priority of tasks to optimize the maintaining process can be made. For this
level, proper info-graphics are necessary to completely transfer acquired
knowledge to the users.
Cognition

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The configuration level is the feedback from cyber space to physical space
and acts as supervisory control to make machines self-configure and self-
adaptive. This stage acts as resilience control system (RCS) to apply the
corrective and preventive decisions, which has been made in cognition
level, to the monitored system.
Configuration

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At the component stage, once the sensory data
from critical components are converted into
information, a cyber-twin of each component
becomes responsible for capturing time machine
records and synthesizing future steps to provide
self-awareness and self-prediction. At the machine
stage, more advanced machine data, e.g., controller
parameters, are aggregated to the components’
information to monitor the status and generate the
cyber-twin of each particular machine. These
machine twins in CPS provide additional self-
comparison capability. At the production system
stage, the aggregated knowledge from components
and machine level information provide self-
configurability and self-maintainability to the
factory. This level of knowledge guarantees a
worry-free and near-zero downtime production and
provides optimized production planning and
inventory management plans.
Implementation of 5C CPS Architecture in Factories

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CPS-Based Smart Machines
Machining processes in the manufacturing industry represent a highly dynamic and
complex situation for condition-based maintenance and prognosis and health
management (PHM). A computer numerically controlled (CNC) machine can usually
handle a wide range of materials with different hardness and geometric shapes and
consequently requires different combinations of machine tool and cutting
parameters to operate. Traditional PHM strategies are usually developed for a
limited range of machine types and working conditions and cannot handle an entire
manufacturing floor where machines can be utilized under a wide range of working
regimes that cannot be modeled comprehensively beforehand. A CPS framework
with a 5C structure is a better solution. A CPS for machine tools can be used to
process and analyze machining data and to evaluate the health condition of critical
components (e.g., tool cutter), improving the overall equipment efficiency and
reliability by predicting upcoming failures, scheduling maintenance beforehand and
controlling processes adaptively.

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Overall CPS setup for band saw machines

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Adaptive health state recognition for blade condition
CPS-Based Smart Machines

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User interface for real-time access of machine health information

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Adaptive Clustering for Self-Aware Machine Analytics
The effectiveness of the proposed CPS structure relies on the performance of the data analysis functions
deployed at the cyber level. The cyber level serves as a bridge connecting the lower level data acquisition and
upper-level cognition functions; it is required to autonomously summarize, learn and accumulate system
knowledge based on data collected from a group of machines. The system knowledge includes possible working
regimes, machine condition, failure modes and degradation patterns; these, in turn, are used by cognition and
reconfiguration functions for optimization and failure avoidance. Because of complications in machine
configuration and usage patterns, autonomous data processing and machine learning are of high priority,
especially as the traditional ad hoc algorithm model cannot be applied to complex or even unexpected situations.
A two-step autonomous machine learning and knowledge extraction methodology has been proposed to perform
health assessment and prognostics within the CPS structure.
+ Similarity-Based Clustering for Machine Condition and Working Regimes
+ Prognostics of Machine Health under Complex and Multi-regime Conditions

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Similarity-Based Clustering for Machine Condition and Working Regimes
Utilization matrix-based prognostics
After the health condition and the working
regimes are identified for each machine, the
next step is to predict the Remaining Useful Life
(RUL). First, the relationship between
machine degradation and the utilization
(stress) history is built using utilization history
and measurement data. Many existing
prediction algorithms fail to perform well for
in-field machines because they cannot handle
complex working regimes and may alter the
actual degradation pattern from lab tests. The
proposed prediction method is based on the
fact that degradation is caused by more than
just time. Other parameters such as stress
participate in machine degradation. Therefore,
a general-purpose prediction algorithm has to
be based on stress vs. life relationship. The
workflow of the proposed methodology is
presented.

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A sample stress matrix for rotating machinery

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For some systems, such as CNC machines, more dimensions (e.g., material hardness,
machining parameters, volume of removed material, etc.) need to be added to the stress
matrix to cover all major factors that cause degradation. After the stress matrix is defined,
machine learning algorithms such as Bayesian belief network and hidden Markov model
can be used to relate the different degradation rates observed in a fleet of equipment to
the corresponding stress history. Eventually, a degradation rate as shown in Table can be
generated and used for prediction under different usage patterns in real-world
applications.

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• Regression models: Using regression algorithms like linear regression, SVM, neural networks,
etc. to predict RUL based on input features.
• Kalman filter: Using Kalman filter to estimate system state and predict degradation trend.
• Markov models: Building hidden Markov models to model hidden states of the system and
predict transition probabilities between states.
• Neural network regression: Using neural network architectures to learn nonlinear relationship
between input data and RUL.
• RNN neural networks: RNNs like LSTM, GRU can model time series data and predict trends.
• CNN: Combining CNN and RNN to extract image features and predict RUL.
Some common methods for predicting RUL (Remaining Useful Life):

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Classification of CPS in Context of Industry 4.0
Cyber-physical system in a smart factory
The basic aim of Industry 4.0 is to ensure
global competitiveness by adapting
manufacturing units to meet changing
customer requirements within the shortest
possible time. This adaptation integrates all
upstream and downstream stages of
production, and only products that are
definitely needed are produced. The
optimal integration of people with their
particular skills makes the adaptation
possible.
One approach to the need for individualization and flexibilization involves networking machines,
equipment, tools, storage systems and emerging products in the IoT; a factory networked in such a way is
termed a “smart factory.”

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Cyber-Physical Systems (CPS) in Industry 4.0 Vision
Cyber-physical systems in the Industry 4.0
vision are based on the latest control
systems, embedded software systems and
an IP address. In the Industry 4.0 context of
mechanics, engineering and so on, CPS are
the next stage in an ongoing integration of
functions. More specifically, the ongoing
improvement driving Industry 4.0 started
with mechanical systems, moved to
mechatronics (e.g., controllers, sensors,
actuators) and adaptronics (i.e., active and
adaptive mechanical systems) and is now
entering the CPS stage

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Cyber-physical system essentially
enables us to make industrial systems
able to communicate and network,
adding to existing manufacturing
possibilities. New possibilities are
emerging in areas such as structural
health monitoring, track and trace,
remote diagnosis, remote services,
remote control, condition monitoring,
systems health monitoring and so forth.
With networked and communicating
cyber physical modules and systems,
connected or smart factories, smart
health, smart cities, smart logistics and
so on have become a real possibility
Cyber-Physical Systems (CPS) in Industry 4.0 Vision

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Cyber-Physical Systems: Summary of Key Characteristics
• CPS represent an evolution in manufacturing, mechanics and engineering. The essential
dimensions are the bridging of digital and physical worlds, made possible by Internet technology
and the bridging or convergence of IT and operational technology (OT).
• CPS can communicate. They have intelligent control systems, embedded software and
communication capabilities and can be connected in a network of other cyber-physical systems.
• CPS can be uniquely identified. They have an IP (Internet protocol) address; they use Internet
technology and are part of an Internet of Everything in which they can be uniquely addressed (each
system has an identifier).
• CPS have controllers, sensors and actuators. Although this was already the case before CPS
(see earlier discussion of mechatronics and adaptronics), the IoT increases their importance.
• CPS are the basic building blocks of Industry 4.0 and the enablers of additional capabilities in
manufacturing (and beyond) such as track and trace and remote control.
• CPS enable smart factories, smart logistics (Logistics 4.0) and other smart areas of applications,
among others.
• CPS include simulation and twin models, smart analytics and self-awareness (self-configuration).

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Operational Technology and Information Technology
Integrated OT-IT system

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- Greater scope of impact
- Physical risks and safety
- Outdated systems or
custom configurations
-Physical risks and safety
-Productivity and quality
control
-Data leaks
-Working with IT
IT and OT meet and converge
in industrial Internet of Things

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Key components of any security solutions OT-IT in IIoT
- Identifying and authenticating all devices and machines within the system,
both in manufacturing plants and in the field, to ensure only approved devices
and systems are communicating with each other. This would mitigate the risk
of a hacker inserting a rogue, untrusted device into the network and taking
control of any systems or machines.
- Encrypting all communications between devices to ensure the privacy of the
data being transmitted.
- Ensuring the integrity of the data generated from these systems. Smart
analytics are a major driver in the adoption of the industrial Internet, but those
analytics are worthless if the data are inaccurate.
- Enabling the ability to perform remote upgrades down the road, if the
manufactured goods contain software or firmware themselves, and ensuring the
integrity of those updates.

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Platforms
•Operating based on the internet and digital
technology.
•Connecting many users/organizations through
technology.
•Allowing interaction, transactions and value
exchanges between parties.
•Collecting and integrating data from user
activities.
•Having tools, APIs for users to build
applications on the platform.

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Digital platform
• Provide an integrated service view of businesses and
unified workflows.
• Seamlessly incorporate transformations such as context,
personalization and proximity.
• Promote constant learning and thinking of customer
behaviors.
• Permit real-time decision-making based on the business
possibilities exposed by various systems.
• Safeguard user data, user context and user presence.
• Suggest innovations to solve business problems.
• Allow multiple partners to orchestrate an integrated
solution.
• Can run and sustain a successful operations model.
• Understand and predict customers.
• Target and reach customers.
• Provide personalized experiences.
• Sense environment and context.
• Rapidly orchestrate workflows.
• Bring synergy between various customer-centric themes

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Exercise
Apply CPS to your manufacturing process
+ Build CPS 5C Level Architecture
+ Presenting components and detailed steps at each level

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End of chapter 2

Chương 2 - CPS.pdf

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Chương 2 - CPS.pdf