In Vitro Test Bench with Intelligent Behavior to Ventricular Assist

Devices

Jeferson Cerqueira Dias

, Jônatas Cerqueira Dias

, Marcelo Barbosa

, Diolino José Santos Filho

Fabrício Junqueira

, Paulo Eigi Miyagi

, Jose Roberto Cardoso

Department of Mechatronics Engineering and Mechanic Systems,

University of São Paulo - USP, Prof. Luciano Gualberto Avenue, 380, São Paulo, Brazil

Department of Energy Engineering and Electrical Automation,

University of São Paulo - USP, Prof. Luciano Gualberto Avenue, 380, São Paulo, Brazil

Keywords: Intelligent Behavior, Test Bench, Ventricular Assist Device, Reliability, In Vitro Test.

Abstract: The abstract The Ventricular Assist Device (VAD) is a mechatronic device used to treat patients with heart

failure who are able to use them in short- and long-term strategies. However, with increasing population

longevity, long-term use has been intensified. Thus, the development of resources that improve the

robustness and reliability of these devices is justified. This work proposes an in vitro test bench with

intelligent behaviour that through a systematic of protocols for the collection, treatment and monitoring of

reliability data, coming from standard curves of monitored variables, such as: flow, pressure, vibration,

rotation, density, viscosity and temperature, provides a decision support system with user friendly interface

for verification, validation and certification of VAD. The proposed method is descriptive of an in vitro test

bed model for VAD that considers the use of Petri net for validation of the dynamic behaviour in front of

the variables and a decision support system based on big data analytics technology with extraction of dada,

which subsidizes intelligent behaviour. The proposed model is consistent with the bibliographic base and its

validation. The Petri net allows confirming its application in the decision making, with intelligent

behaviour, from the data mining.

1 INTRODUCTION

In the last twenty years we have witnessed a

technological evolution in the development of

products and processes, from consumer goods to

industrial automation systems. One of the most

determining characteristics observed in this evolution

is artificial intelligence, although there is no common

definition among researchers. We can describe

intelligence as the ability to understand events in the

surrounding environment and to process information

rationally in order to react to events in this

environment (Flyn, 2009; Khalfa, 1995).

Over the years, this characteristic has been

moving towards consumer products, something

perceived in 1991 by Mark Weiser, considered the

forerunner of ubiquitous computing, of which

technology is present everywhere (present in objects

such as clothing labels , switches, pens, among

others), but it is transparent to the user (Weiser,

1991). These technologies enable physical products

to be filled with intelligence, sensitivity, and

communication skills. This creates a new product

category called "Smart Products" (Mühlhäuser,

2007; Sharma, et al., 2017; Kuhn et al., 2018) that is,

systems that behave rationally (Russell; Norvig,

2013).

This evolution is based on the microelectronics

with the recent innovations in microchips, allowing

to create smaller sensors that through computers,

control actuators, allowing, thus, to create

mechanisms of intelligent mechatronic systems.

The development of mechatronic devices

encompasses the design that combines: mechanical

structures with electronic control circuits (Silva,

2005). Over the years, these devices have become

smarter, utilizing optical resources, renewable

energy, computer science, automated control and

other disciplines, as well as new software

technologies, internet of things (IoT), robotics and

manufacturing integrated by computer (Atoche and

Marrufo, 2011).

Dias, J., Dias, J., Barbosa, M., Filho, D., Junqueira, F., Miyagi, P. and Cardoso, J.

In Vitro Test Bench with Intelligent Behavior to Ventricular Assist Devices.

DOI: 10.5220/0006849101270134

In Proceedings of the 15th International Conference on Informatics in Control, Automation and Robotics (ICINCO 2018) - Volume 1, pages 127-134

ISBN: 978-989-758-321-6

127

This article proposes the use of this technological

evolution in the study of Ventricular Assist Devices

(VAD), since according to the World Health

Organization in 2016 of the 38 million deaths per

year, classified as "non-transmissible", 17.5 million

(46%) were cardiovascular diseases. Of these, 7.4

million were coronary heart disease and 6.7 million

strokes (World Health Organization, 2016) and a

growth of more than 23.6 million of these diseases is

planned for 2030 (Mozaffarian et al., 2016).

Cardiovascular mortality rates remain the leading

cause of death worldwide (Nichols et al., 2015).

The implantation of a VAD has proven to be a

successful option for the treatment of patients with

heart failure, that is, it can be implanted to ensure

that the patient can wait until a heart transplant, and

in this case, this practice is known as "a bridge to the

transplant”, or when the patient has a high rejection

rate for the transplant, the device is implanted as a

permanent solution and in this case this practice is

known as "destiny therapy" (Murala and Si, 2017)

(Healy et al., 2016) (Drakos et al., 2010). One of the

issues that has intensified the use of these devices as

target therapy is the imbalance between the number

of donors and the number of recipients for heart

transplantation (Christie et al., 2012).

The increasing longevity of patients transplanted

with VADs as a target therapy recently (Goldstein

et al., 2018) (Cowger et al., 2018) justifies the

development of features that improve the robustness

and reliability of these devices. In this sense, there

are several approaches that can be used for

implantable medical devices, ISO standards, ASTM

standards, in vivo animal study, cadaver study,

among others, as well as specific methods such as

the characterization and demonstration of reliability

(Zhang; Jiang, 2017). Guidelines for validation

procedures have also been developed for the reliable

procedure for validating medical devices. The

accelerated tests used as tools to verify the

correctness of the manufacturing of electronic

components and microelectronics are a good option

to obtain and prove reliability data (Valis; Vintr;

Koucky, 2010) (Evans; Sinha, 2018).

For ventricular assist devices some research has

been developed, such as the use of an in vitro

hemolysis test bench for development evaluation

and safety regulation to check the

hemocompatibility of medical devices such as

VADs that have contact with blood (Olia et al.,

2017). To model the arterial and pulmonary

hemodynamic behavior of the circulatory system

was developed by (Mueller et al., 2017) a bench of

tests that presented good results for reproduction of

the system. For a total heart without valves,

compatible with pediatric population, was presented

by (Tozzi et al. 2017) a bench of tests that presented

encouraging results, despite issues related to

durability, and the need for confirmation of results

with in vivo test. The bench test to simulate in vitro

conditions of biomedical metal alloys for implants of

devices made by (Ijaz et al., 2017) demonstrated

satisfactory results for fatigue resistance of Ti-Nb-Zr

alloys when compared with Ti alloys.

Improving the reliability of VADs allows a

constant search, however, the absence of failure data

from the device or its components for a performance

analysis is a gap that can be improved with an in

vitro test bed of VADs. This work within a control

hierarchy proposes the testing and monitoring of

devices with a continuous collection of context data,

which will be analyzed and treated against reference

correlation curves of variables (pressure, flow,

vibration, temperature, viscosity, density , rotation,

and power) that will allow us to identify the

description of the occurrence of failures from which

the system transfers the information to a cloud

knowledge base for analysis of standards according

to Big Data Analytics technologies. With this

information an intelligent module based in Artificial

Intelligence (AI) can readjust the set-up variables by

adjusting the control to a safe state of operational

functionality. Allied to this system a remote

operator, with support for decision making, can also

monitor and analyze the level of system failure and

operate from a smart device.

In this prospective work will be used the

modeling of an in vitro test bench for VAD in Petri

net to represent its behavior and for validation of the

model.

2 TEST BENCH

This work’s proposed test bench is composed by two

tanks (T1 and T2) that are responsible for the storage

of the fluid that travels through the system. The fluid

is transported from tank T1 to tank T2. The transport

is carried out from a pump (B1), which imposes

energy on the system. During the test bench

operation process, sensors are used to collect the

main variables involved in the system (Figure 1).

Sensors collect fluid pressure at tank outlet T1,

engine speed M1 from pump B1, pump vibration

B1, electric motor current M1 from pump B1, fluid

pressure at pump outlet B1, fluid flow at outlet of

the pump B1, the temperature of the fluid at the

outlet of the pump B1, the position of the motor M2

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Figure 1: VAD In vitro Test Bench Process Diagram and

Instrumentation.

of the valve V1, the electric current in the motor M2

of the valve V1 and the pressure of the fluid at the

inlet of the tank T2.

To perform, the test bench has an M1 motor

responsible for the rotation control of the pump B1

and an M2 motor responsible for the position control

of the valve V1 that allows the flow control of the

fluid. To collect the data emitted by the sensors and

impose the new references to the actuators, a

communication manager is necessary for distribution

of the information.

3 PROPOSED CONTROL

SYSTEM

In the development of the proposed control system,

it is approached the concept that the test bench does

not have an on-board control and monitoring system.

In this way, the control system acts on a remote

server that collects information from the test bench

sensors for monitoring information on mobile /

desktop devices.

The information that travels from the test bench

to the supervision is carried out from the concept of

Publish / Subscribe (Sonawala et al, 2017). This

communication approach is characterized by the

asynchronous exchange of events between devices

and applications that make up the system, so the

applications need to receive a certain type of event

subscribers that is originated by another application

and event publisher. The main characteristics of

publish / subscribe communication are to eliminate

the direct coupling between the applications and to

allow the selective dissemination of information

based on the interests of the system (Lopes, Bock

and Gómez, 2017).

The actuating commands for parameterization of

the Set Ups, the motor rotation (M1) of the pump

(B2) and the position of the motor (M2) of the valve

(V1) are performed by two agents: a) from a mobile

/ desktop, in this case the user interferes directly in

the event management and b) by the AI module,

which searches through the learning and the patterns

identified by the Big Data Analytics module, the

best conditions of Set Up of the control variables to

allow the greater life of devices (VADs) under test.

The information system performs all demand

processing to inform the local communication

manager that replicates the data to the local control

system reference. For monitoring, the

communication manager is responsible for reading

the data emitted by the sensors from the test bench

and publishing them from the physical event

manager to the collection and storage processes in

the database.

To access information about test bench

operation, users can: require the performance and

requests data directly from the user interface, request

query information from the Big Data Analytics and

AI modules, and in vitro make operational decisions

through the mobile / desktop module (Figure 2).

Figure 2: Architecture of proposed control.

All collection, storage, monitoring, and

command features are centralized in the use-case

manager. This module is responsible for controlling

the information demanded by sensors, actuators,

users and AI. Below is a representation of the

proposed control architecture.

4 SYSTEM MODELING

Petri nets are tools that use mathematical models and

graphics that apply to a system that, by its

In Vitro Test Bench with Intelligent Behavior to Ventricular Assist Devices

129

characteristics, allows modeling of system behavior,

as well as graphical visualization, synchronization,

parallel data processing and observation of

competing systems (Murata, 1989). These

characteristics allow to follow the variations of the

dynamic behavior of the modeled system, which in

this case will be the test bench.

Petri nets have been used in various applications,

for example, to model complex biological systems

of yeast cell cycles based on multisite

phosphorylation (Herajy; Liu; Heiner, 2018),

modelling and analysis of traffic signal priority

control systems (An et al., 2018) in accidents, in the

cascade analysis for vapor cloud explosions of

flammable fuels (Zhou; Reniers, 2017),

interoperability test bench model for embedded train

control system (Yuan et al., 2010), and, approach for

the evaluation of the reliability of the final elements

of the Instrumented Security System with time-

dependent failure rates (Wu et al., 2018).

5 METHOD

The research has a theoretical and quantitative

descriptive methodological approach, since it aims

to discover the existence of associations and

correlations between variables (Mattar, Oliveira and

Motta, 2014) and prospective in the timeline

(Fontenelles et al., 2009) when a method of

experimentation is proposed by means of in vitro

models. Modeling through the Petri net completes

the In vitro model, allowing the simulation of the

process in a computational model of a real system,

conducting experiments with this model with the

purpose of understanding and validating its behavior

(Centeno, 1996).

On the other hand, the technical procedures,

related to the method, are the application of the Petri

net to model the dynamic behavior of the variables

of the VADs in tests, obtaining the behavior of faults

by deviations when compared to the standard curves

of these variables (pressure, flow, vibration,

temperature, viscosity, density, rotation and energy).

Decision making is based on maintaining the

longevity of the life of the device being tested, in

which two agents can act: a) an AI module that is

based on machine learning and b) a human assistant.

5.1 Data Colleting and Handling

The "context" information source (Wan, 2009) of the

assisted device is connected to the sensors in the

"Test Bench", from which it transfers this

information to a real-time cloud knowledge base.

This knowledge base is handled by a standards

analyzer for knowledge discovery using Big Data

Analytics technologies.

In this paper "context" is any information that

can be used to characterize the situation of an entity

(object of interest), it is also known as sentient

computing (Andrade, 2015). Thus, an entity can be a

person, a place, or an object that is considered

relevant to the interaction between a user and an

application, including the user and the applications

themselves (Schilit; Adams; Want, 1994).

5.2 Data Analysis

The Big Data Analytics’ approaches to pattern

discovery are based on: (i) Descriptive Analysis -

Real-time understanding of events so that immediate

decisions can be made. The descriptive analysis

works with data history, crossing information with

the objective of generating a clear and precise

panorama of the relevant themes for the present

moment, without necessarily relating it to past or

future patterns (Marquesone, 2016). (ii) Diagnostic

Analysis: Its objective is to understand the relation

of cause and effect (Who, When, How, Where and

Why) perceived over time and its possibilities. It

works based on the collection of data related to a

certain subject, crossing information in order to

understand which factors influenced the current

result (Marquesone, 2016). (iii) Predictive Analysis:

Known for "predicting" the future, we use data

mining: statistical and historical data to know future

trends (Marquesone, 2016). (iv) Prescription

Analysis: Very confused with the predictive

analysis, the prescriptive analysis works with the

same logic, but with different objectives. While

predictive analysis identifies future trends, the

prescriptive outlines the possible consequences of

each action. It is a way of defining which choice will

be most effective in a particular situation

(Marquesone, 2016).

6 SYSTEM MODELLING

The modeling developed for the system uses a

systematic and refinement approach. Using a

combination of the work of Villani (2004) to

perform the modeling of the supervisory control and

monitoring system of the test bench (Figure 3). In

step 1, fluid flow modeling is performed by the

bench of tests to characterize the dynamics of the

test bench (Villani et al., 2004).

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Figure 3: PFS model of the test bench.

In step 2 all desired functionalities are described

for the control and monitoring system of the test

bench (Villani et al., 2004). In step 3, the

implementation strategies of the control system are

described, so it is specified how the system should

behave for each use case. In step 4, the modeling

procedures begin by performing the modularization

of the system components. In step 5, the component

models for analysis are integrated. In step 6, the

models are analyzed from the main properties of

Petri nets.

6.1 Step 1: Modeling Material Flows

This step aims to define the boundaries of the test

bench that will be controlled by a command and

monitoring control system. This model is built on

PFS (Product Flow Schema), which is based on the

concept of successive refinement (Villani et al.,

2004). For more information on PFS modeling and

successive refinement, it is recommended to read

Miyagi (1988).

The activities of [Storage of the fluid in tank T1]

and [Storage of the fluid in tank T2] are detailed in

Figure 4 so that no further refinement is required.

For the activity [Transport of T1 to T2 with B1], one

can drill down to another level.

Figure 4: Activity Detail [Transport of T1 to T2 with B1].

From this model it is possible to highlight the

dependencies and inputs and outputs of the

operational process of the test bench.

6.2 Step 2: Definition of Use Cases

1. The system agents (user and AI) can

parameterize the flow reference flowing

through valve V1 and pump motor rotation

reference B1.

Table 1: Assignment of features for each control device.

Devices

Activities

Sensors

-Publish value collected to the

communication manager

Actuators

-Receive new reference from the

communication manager

Communication

Manager

-Receive value collected from the

sensors

-Receive new physical event manager

reference;

-Publish value collected from sensors

to the physical event manager;

-Publish new reference received by

the physical event manager to the

actuators;

Physical Event

Manager

-Direct publication and receipt of the

use case manager and communication

manager;

Use Event

Manager

-Direct publication and receipt of the

mobile / desktop devices and use-

case manager;

Mobile / desktop

device

-Request data from the sensors stored

in the database;

-Amply new reference to actuators;

-Measure new sensor value limits;

-Receive alert for abnormal values;

Database

-Save data collected from sensors;

-Save sensor limit value;

-Store new actuators reference;

-Consult stored values of the sensors;

Use case

manager

-Receives value collected in the

sensor of the physical event manager,

validates the measured value to

ensure that it is within the accepted

limits of the standard curve of pump

B1 and stores in the database;

-Receives new reference value from

the actuators of the event manager,

stores it in the database and publishes

it to the physical event manager;

-Receives new threshold value from

the sensors of the event manager and

stores it in the database;

-Receive request to query the sensor

data in the database of the event

manager and publish the data in the

event manager;

2. System agents (user and AI) can

parameterize a pressure value limit on

sensors PT1, PT2 and PT3, vibration on

sensor VT1, rotation on sensor ST1,

electric current on sensors IT1 and IT2,

temperature on sensor TT1 and the FT1

sensor.

In Vitro Test Bench with Intelligent Behavior to Ventricular Assist Devices

131

3. System agents (user and AI) can monitor

the pressure values in the PT1, PT2 and

PT3 sensors, vibration in the VT1 sensor,

rotation in the ST1 sensor, electric current

in the IT1 and IT2 sensors, temperature in

the TT1 sensor and flow in the FT1 sensor.

4. The supervisory system collects the

pressure values on the sensors PT1, PT2

and PT3, vibration on the VT1 sensor,

rotation on the sensor ST1, electric current

on the sensors IT1 and IT2, temperature on

the sensor TT1 and flow on the sensor FT1

and stores in the bank data.

6.3 Step 3: Specification of Control

Strategies to Be Modeled

From the proposed control architecture, we identify

mobile / desktop devices, use case manager,

communication manager, database, test bench,

physical event manager and event manager. Table 1

specifies the assignment of functionality for each

device.

6.4 Step 4: Construction of

Components

From the control strategies (step 3) and proposed

control architecture, the modelling procedures are

started. For each device specified in the strategies

there will be a Petri net. In this way, integrating the

devices according to their needs, one must build the

Petri net models of the mobile / desktop device, use

case manager, database, event manager, physical

event manager and communication manager.

The final goal of device integration can be seen in

Figure 5.

Figure 5: Interactions of the supervisory control.

6.5 Step 5: Integrated Component

Modelling

In the integrated modeling, the component models

are combined to perform the Petri nets verification

from their properties for each of the system use

cases: collection, validation and storage of the

sensor data.

6.6 Step 6: Analysis of Models

The analysis of the models is performed for each of

the use cases, so that the properties of the mark

diagram are evaluated, it is verified if the modelled

Petri net is limited, safe, alive and bootable.

7 RESULTS AND DISCUSSION

The research sought to analyse the automation of a

test bench, using resources: modelling of dynamic

behavior, through the Petri net; to provide intelligent

behavior with the use of AI and machine learning, in

face of the changes of the monitored variables; as

well as the use of diagnostic and prognostic features

in Big Data Analytics technology for data analysis

and treatment.

From the analysis of the properties of the models

and simulations performed in the validation of each

of the specified use cases for the control system, it is

identified that the proposed system meets the

functional requirements according to the model

properties checks. However, it requires simulation

tests for adjustments in the predictive and diagnostic

model, which still causes conflicts with the user's

decision regarding the adjustments made by the AI

model.

8 CONCLUSIONS

The purpose of this work is to explore the benefits of

the introduction of intelligent control systems,

applied to a test bench of ventricular assist devices

(VAD), allowing to these “in test” devices the

greatest possible longevity of use, at the same time

in which the behavior variations are identified,

observing the changes in the dependent variables,

while controlling the independent ones, by means of

the set-up change of these variables.

The final result obtained is: a knowledge

database containing: a) failure data and respective

behaviors over time, allowing intelligent control to

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predict future consequences, from tests of new

devices; b) a set of improvements that can be applied

to the design of new devices, allowing more durable

devices; c) refinement of a learning system as it

continually evaluates the test results, compared to

the designs of each device, e.g. device with such

design characteristics as: structure, form of

construction, materials used, among others, have

tendencies of behavior, which can lead to a set of

predictive actions; d) the subsidy and experience for

the elaboration of a new challenge, of a robotic

device (VAD) that can, depending on the construct-

ion project, use this knowledge base to provide

longevity of use of its resources and limitations,

consequently creating longevity of its host.

A robotic VAD could have adaptive and resilient

behavior, being flexible without the need to be

reprogrammed, since robots are designed to be able

to perform various tasks based on simple

programming (Niku, 2013).

With these elements, it can be concluded that the

control system for control and monitoring of the test

bench provides significant gains for the detailed

study of the operation of VADs. With the collection

of more flexible information and interfaces it is

possible to carry out analysis and data collection that

help in the continuous improvement of these

devices.

ACKNOWLEDGEMENTS

The authors thanks FAPESP, CNPQ e CAPES for

supporting this research.

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