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Knowledge Engineering approach & Artificial Intelligence Solutions
The border-line between the up-to-date APC and AI approach, is
hard to distinguish in most of common complex process control
cases … (apart from a general not widespread familiarity with
this second…)
However, another point of view may be:
- Is the “deterministic computation” a near real-world app2roach
rather than human heuristic/non deterministic computation?
- When we take a decision, have we before solved a math
formula?
If the answer is no, in this case most probably, we have applied
Knowledge Engineering approach.
Remarks:
Where Artificial intelligence is better applicable in Process Control?
1) In “Early Warning” Sensorial Process Control application: where the ability to timely detect critical event require data fusion from different multimedia data sources (signals, imaging , etc).
2) In “Evolutive” Process Control (like biological, environmental, Constrain-Based Scheduling in closed-loop processing, etc.) where often what we have to control is a multi-disciplinary compound of quali-quantitative parameters and behaviors that not recognizable with traditional instrumentation (incremental learning is required).
3) In “Fault Detection & Classification ” in a Not Well-Known or New Process, when there is not a plant history database or the operative process functionalities are together not correlated (in this case a multi-classifier or pattern recognition is required).
4) In a Not-Invasive Revaluation (upgrading) of Process Control Assets when a Process Know-How is a Competitive Value that need to be exploited by a Knowledge Based ICT system.
Knowledge Engineering starting by Knowledge Extraction
How Artificial intelligence is better applicable in Process Control?
• DATA MINING Methodologies on domain of interest
• HEURISTIC approach to extract basic rules of domain
• ANN used to extract rules and hidden correlation from data sources
• TREND ANALYSIS Mythologies to understand peculiar dynamics
• KNOWLEDGE MODELING & COGNITIVE MAP on “XBASE” development
Knowledge Extraction (other Tools)
How Artificial intelligence is better applicable in Process Control?
Fault Prediction Using Wavelets
KE by primitive identification
1) DATA VALIDATION & SETTING: []n,1 [] n,m [] m,1 Sustainable Validation Data Set
On-Line INPUT & Inferential Evaluation Input
Vector Matrix Vector
2) DATA NORMALIZATION: []m,1 [] m,1 = []m,1
3) DATA INFERENTIATION: []m,1 [] n,m = []m,1
4) DATA INTEGRATION: [ A] [ B] = [ AB]
5) DATA FUSION: [ AB ]k,1 []k,1 = []k,1
6) DATA DENORMALIZATION: [ ]k,1 []k,1= []k,1
Not Deterministic
Knowledge from
Process Understanding
ON-LINE INPUT
the Process Need
(Data you need from sensors)
Dynamic Weight Functions
Adaptive
INFERENTIAL ENGINE
XBASE Approach to Knowledge Modelling:
How Artificial intelligence is better applicable in Process Control?
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A Non-Deterministic Approach, in Process Monitoring & Control, means to compute with the embedded knowledge contents in data (by Knowledge Modeling) and not only with the “numbers” they self represent.
X(t)
Y (t)
Z (t)
Knowledge Model
In a deterministic approach, we have X(t), Y(t) and Z(t) as 3 on-line deterministic measurements … In a non-deterministic approach, we consider also the
distance between P(x,y,z) and the Knowledge Model Surface Knowledge Based as Global (non deterministic) Indicator (see the pic on side). In general, we have not a single correspondence between each single input/output data:
P(x,y,x)
Non– Deterministic Approach at a glance…
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Controller
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An example of Expert Controller Node
Intelligent Monitoring & (Learning) Control System
Adaptive Process Control Strategy
• In Strategic Approach Designing to Process Control
• In Non-Deterministic Process Modeling (f.e.: Biologic)
• In Risk/Fault Diagnosis functionalities
• In Decision Support functionalities
• In Dynamic Multi-constrains Scheduling
• In High Dynamic Forecasting functionality (customer profiling)
• In Incremental Learning and Knowledge exploitation
• In Evolving Multi-Classifier and Multimedia Pattern Recognition
• In a Cheaper Upgrading of Exiting Process Control Systems
When Artificial intelligence is better applicable in Process Control?
• In Tactical Approach Designing to Process Control
• In Modeling of the most part of Deterministic Process Control
• In Accounting, handling of “money” or Financial Management
• When we have not an operative expertise in AI handling
• When a deep and timely interaction with process is not required
(when “what” approach is enough instead than “why”)
When Artificial intelligence is not wise in Process Control?
Multidisciplinary Expertise Conventional Expertise
Final Target
Final Target
A metaphor may help us…
