PriMa: a prescriptive maintenance model for cyber-physical production systems

Figures & data

Table 1. Challenges for design and realization of CPPS (Adapted from (CPS Summit 2008), (Lee and Seshia 2015), (Seidenberg and Ansari 2017)).

	Challenges	Description	Type
			Technological	Non-Technological
I	Compositionality	Compositionality problems may cause due to the integration of heterogeneous cyber and physical elements of CPPS, i.e. in system integration of both subsystems and Cyber-Physical Systems-of-Systems (CPSoS).	✓
II	Distributed sensing, computation and control	Key problems include (semi-)real-time collection of adequate information, processing and asserting control in a distributed environment.	✓
III	Physical interfaces and integration	An essential feature of CPPS is to be able to contact with the physical world using various sensor technologies.	✓
IV	Human interfaces and integration	Another essential feature of CPPS is the ability to effectively communicate with various types of end-users in diverse qualification and competence levels through use of human-machine interfaces.	✓	✓
V	Extracting knowledge from heterogeneous data sources	Processing raw-data collected in structured, semi-structured and non-structured format as well as various quality and veracity levels, and extracting knowledge for supporting decision-making and learning from each decision instance, require deploying smart data analytics and Artificial Intelligence (AI) techniques, in particular predictive data analytics and deep learning.	✓
VI	Modeling and analysis of physical and cyber components	A massive complexity is exhibited through modeling and analysis of heterogeneous physical and cyber components with different notions of time, across different scales, and integration of the feedback collected from them.	✓
VII	Privacy, trust, security	Application of CPPS in diverse sectors raises new issues in privacy, trust and security, inter alia, revealing information from cyber-physical-socio space may require new rules on accessibility and transparency of information, i.e. what information can be hidden. In addition, new kinds of physical and cyber-physical attacks are possible e.g. in the context of smart and connected factories, i.e. there is a demand to new concepts and tools for cyber-security.	✓	✓
VIII	Robustness, adaptation, reconfiguration	CPPS operates in dynamic (production) environments and thus needs to handle uncertain situations and disturbances without affecting the outcome quality. In addition, CPPS should be reconfigurable and adaptive to deal with (unexpected) faults in both physical and cyber levels.	✓
IX	Software technology	Development and deployment of CPPS require new programming languages, which could deal with complexity of cyber-physical spaces. In addition, CPPS requires integrated software solutions to support performance planning, monitoring and controlling of CPPS and dealing with high volume of (real-time) data fellow among sub-systems, systems-of-systems and environment.	✓
X	Verification, testing, (safety) certification and guidelines	CPPS requires new approaches for verification and testing to check and regulate potential events such as physical or cyber related faults by comparing with certain standards derived from historical patterns, physical correlations and characteristics of a CPPS. For this purpose, a detection, learning and reasoning module should be developed to assure correctness of decisions and promote learning from former ones. Application of CPPS in diverse industrial sectors also requires handling safety certifications, e.g. to identify hazard sources and the way to deal with them in accordance with health and safety legal requirements in the workplace.	✓	✓
XI	Societal impact	Social aspects of CPPS may also affect the design, verifiability, validation, operation, privacy, trust, and fault tolerance. Especially, developing human-centered CPPS confronts several societal, cultural and ethical resistance to technological change e.g. due to the risks that robots or intelligent systems may take over human jobs and authorities in the workplace.		✓

Figure 1. The basic model of KBM – Adopted from (Pawellek 2013).

Figure 2. Evolution of KBM – Maturity and complexity levels.

Figure 3. Overall architecture of PriMa.

Figure 4. PriMa’s database schema – Interlinking multimodal data and building a scalable data warehouse.

Table 2. Four main families of machine learning algorithms (Adopted from (Kelleher, Mac Namee, and D’Arcy, 2015), (Ansari et al. 2018)).

	Learning Approach	Description	Example of algorithm
Machine Learning Families	Information-based Learning	Employing concepts from information theory to build models.	Decision Trees
	Similarity-based Learning	Building a model based on comparing features of known and unknown objects, or measuring similarity between past and forthcoming occurrences.	k nearest neighbor (k-NN), Case-Based Learning
	Probability-based Learning	Building a model based on measuring how likely it is that some event will occur.	Dynamic Bayesian Network
	Error-based Learning	Building a model based on minimizing the total error through a set of training instances.	Multivariable linear regression

Figure 5. Relation between PriMa and PPC.

Figure 6. Different data perspectives of the industrial use-case. Reprinted from (Glawar et al. 2016a).

Figure 7. Different types of component behaviour.

Table 3. Relevant data sources and information.

Data Source	Relevant Information	Quality/Granularity of Data
ERP System	Cost of spare parts, availability of spare parts	Real time information
ERP System MES System	Hourly cost rates	Cost rates for skill groups
	Total downtime of machine & machine performance (Overall Equipment Effectiveness (OEE))	Downtime manually entered by operator
MES System Maintenance Management Software (CMMS)	Historical production programme, (planned) future production program	Planned/Produced parts (per product type) per machine per shift
	Process parameters	Process parameters for each manufacturing job
	Periodic service plans	Service intervals for each components, planned service dates (monthly, quarterly, yearly)
Access data base for failure documentation	Documented Failure events (Report of failure event, and documentation of failure handling)	Manual documentation by operator (full-text)
Access data base for failure documentation Historical Knowledge Data base (Excel)	Time stamp (Report of failure, failure handling finished)	Manual documented by operator
	Failure classification	Rough classification (Mechanical, electrical, etc.)
	Historical Documentation of failure events	Analog to Access DB
Quality Control Database	Quality data of products	Measurement of one product of each produced product type per shift (value for each quality criteria)

Figure 8. Four-Step methodology for applying PriMa. Adopted from (Matyas et al. 2017)

Table 4. Target data structure.

Failure documentation	Time stamp	Machine ID	Module	Group	Component	Classification	Root cause
Quality data	Time stamp	Machine ID	Product ID	Quality- factor	Value of Measurement	Target Value	Upper limit	Lower limit
Production Program	Time stamp	Machine ID	Product ID	# Parts
Process data	Time stamp	Machine ID	Product ID	Process Parameter	Value	Target Value	Upper limit	Lower limit

Figure 9. Relevant systems and tools for the operative maintenance processes.

Figure 10. Example for pre-processing of free text passages.

Figure 11. Correlation matrix of the quality factors.

Figure 12. Correlations between quality factors and failure effects as revealed in (Glawar et al. 2016b).

Figure 13. Dynamic calculation of wear – reproduced from (Matyas et al. 2017) & (Glawar et al. 2016a).

Table 5. Defined rule-modules.

Type of rule	Mathematical function	Parameters
Gradient of the regression curve of n-measuring points	$\mathrm{b}=\frac{\frac{{\sum }_{\mathrm{i}=1}^{\mathrm{n}}\left({\mathrm{x}}_{\mathrm{i}}-\bar{\mathrm{x}}\right)\ast \left({\mathrm{y}}_{\mathrm{i}}-\bar{\mathrm{y}}\right)}{\mathrm{n}-1}}{\frac{1}{\mathrm{n}-1}{\sum }_{\mathrm{i}=1}^{\mathrm{n}}{\left({\mathrm{x}}_{\mathrm{i}}-\bar{\mathrm{x}}\right)}^2}$	n = number of measured values yi = considered values xi = 1… n (represents the date of measurements) x = arithmetic mean xi = considered values
Mean value of n measured values	$\bar{y}<\left(>\right)t$$\bar{y}=\frac{1}{n}\sum _{i=1}^n{y}_i$	y = arithmetical mean yi = measured value n = amount of considered measurements t = predefined value
Comparison of variances of measured value	$\frac{S_n^2}{S_N^2}<\left(>\right)t$$S_n^2=\frac{{\sum }_{i=N-n+1}^n{\left(y_i-{\bar{y}}_n\right)}^2}{n-1}$$S_N^2=\frac{{\sum }_{i=1}^N{\left(y_i-{\bar{y}}_n\right)}^2}{N-1}$	sn^2 = empirical variance of N sN^2 = empirical variance of n t = pre-defined value n = considered values (the last n measured values out of N) N = amount of measured values yi yi = considered values yn = arithmetical mean of n yN = arithmetical mean of N
Number of limit violations	$f_n>m$$f_n\sum _{i=1}^n{f}_i$	fi = function describing correlation between value and pre-defined value n = amount of measured values m = amount of measured values exceeding or falling below a pre-defined value
Wear reserve	${\mathrm{w}}_{\mathrm{r}}\le {\mathrm{w}}_{\mathrm{s}}$	wr = actual wear reserve of component ws = pre-defined wear stock

Figure 14. Example of violation of a combination rule – Adapted from (Matyas et al. 2017).

Figure 15. Decision flowchart for prescriptive maintenance decision support.

Table 6. Summary of results regarding the application of PriMa.

Machine Part	Reduction of Downtime	Planned/Unplanned downtime	Behavior type
Tool Spindle (Ref. machine A)	12.1%	8.3%	Load dependent
Tool Spindle (Ref. machine B)	25%	12.5%	Load dependent
Clamping mechanism	14.3%	9.1%	Load independent
Ball screws	23.1%	10.0%	Load dependent

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