Rule-Based Decision Making in Biologically Inspired Condition

Management System

Hariom Dhungana

Department of Mechanical and Marine Engineering,

Western Norway University of Applied Sciences, 5063 Bergen, Norway

Keywords: Condition Monitoring, Diagnostics, Prognostics, Biomimicry, Cognition, Decision Making.

Abstract: Biomimicry is an approach for solving industrial challenges by taking inspiration from bio-organisms’

responses. In an ongoing research project, investigations are being carried out to explore the use of biomimicry

approach for a human-centric condition management system. In this system the decision-making process is

divided into three types procedural decision-making, deliberative decision-making, and argumentative

decision-making. This paper intends to show the experimental verification of rule-based decision making (a

type of procedural decision making) in condition monitoring system using an example of rolling element

bearing. Rule-based decision-making involves using predefined guidelines to make choices, ensuring

structured, consistent, fair, efficient, and unbiased decisions. Vibration sensor data is used from PRONOSTIA

datasets to obtain four useful information’s and using those information’s in fuzzy rules to get decision. The

outcomes indicate the viability of the suggested framework for rule-based decision-making using real-time

vibration data.

1 INTRODUCTION

Biomimicry, the practice of drawing inspiration from

nature to solve human challenges, has found

relevance in the realm of decision-making (The

Biomimicry Institute — Nature-Inspired Innovation,

n.d.). The human brain plays a central role in the

process of decision making. It's a complex and

intricate organ that integrates various cognitive

processes and emotions to evaluate options, weigh

pros and cons, and arrive at a choice. Old brain and

new brain are familiar terms that refer to different

parts of the human brain that evolved at different

times in human evolution. The old brain is

responsible for basic survival functions, while the

new brain is responsible for more complex functions

such as reasoning, thinking, learning, and problem-

solving (Hawkins, 2021). Procedural decision

making involves an old brain while the thoughtful

decision making involves both old brain and new

brain. The old brain carries a cache of “best practices”

that is quick and simple.

Effective decision making in condition

monitoring system aims to reduce unplanned

downtime, extend equipment lifecycles, and optimize

maintenance efforts. Condition-based maintenance

emerged to minimize expenses associated with overly

frequent scheduled maintenance. Within an ongoing

research effort, a human-centred condition

management system framework has been proposed in

(Singh et al., 2024), drawing inspiration from human

cognition. Three decision making techniques named

procedural decision-making, deliberative decision-

making, and argumentative decision-making are

presented for equipment maintenance as in Figure 1.

Procedural decision making is the process of

making decisions based on a predetermined set of

rules, practices, or protocols. It can be modelled either

by rule-based approaches (Singh & Pokhrel, 2018) or

case-based approaches (Kolodner, 2014). It uses

sensory information integration and evaluation to

decide a course of action (Van Der Meer et al., 2012).

It is important for many everyday tasks, such as

recognizing faces, avoiding danger and so on. It

involves the brain's ability to process and interpret

sensory signal and to use that signal to make decision.

It might be useful in ensuring fair and consistent

decisions, but not guarantees to accommodate unique

or unexpected situations in decision-making like

something is better than nothing.

Deliberative decision-making involves a thorough

process where individuals thoughtfully analyse

Dhungana, H.

Rule-Based Decision Making in Biologically Inspired Condition Management System.

DOI: 10.5220/0012461100003636

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 16th International Conference on Agents and Artiﬁcial Intelligence (ICAART 2024) - Volume 3, pages 1245-1254

ISBN: 978-989-758-680-4; ISSN: 2184-433X

1245

options, gather information, weight consequences,

and assess outcomes before making a choice. It

requires conscious thought, reasoning, and differs

from procedural decision-making that relies on swift,

automatic, and emotional reactions. In deliberative

process, the fault finding aims to uncover latent

failures, requiring thorough analysis for optimal

decision-making, reliability assessment, and

streamlined maintenance policy ideal (Junior et al.,

2022). An instance of deliberative decision-making,

involving reliability assessment and optimization of

maintenance policies for the yaw system on a wind

turbine, was showcased in (Catelani et al., 2020).

Balancing procedural and deliberative decision-

making are key for effective choices, harnessing

strengths from both approaches. Argumentative

decision-making involves evaluating options using

structured arguments and evidence, aiming to

enhance decisions by rational and well-justified

selection among alternatives. Arguments serve dual

roles: aiding alternative selection and justifying

adopted choices in both everyday decisions and

critical discussions (Amgoud & Prade, 2009).

Argumentation enhances AI explain ability by

revealing decision steps, offering reasoning amidst

uncertainty, and resolving conflicting information

(Vassiliades et al., 2021). The performance of

decision-making models has been improved by

combining the argumentation by providing human

supervision for image classification and large-scale

real-world semi-autonomous driving in (Fridman et

al., 2019).

Figure 1: Decision making framework proposed in

biologically inspired condition management system.

Immediate maintenance decision is carried by

procedural decision making from initial observation,

and deliberate or thoughtful maintenance decision is

done by using root cause finding, likelihood

estimation, continuous learning, and optimization of

available resources. The main objective of this work

is to show case study of rule-based decision making.

This paper thus investigates the usefulness of the

framework using in vibration signal in bearing. Some

preliminary case study of initial fault identification

was carried out in (Singh et al., n.d.). This work

shows the complete case of rule-based decision-

making using fuzzy rules.

A quick and efficient decision-making system

plays a vital role in enhancing maintenance task

especially for safety critical equipment’s. We argue

that the concept of rule-based decision making is at

the very basis of the quick decision making and that

using limited data through a well-established

technique both safety & security as well as goals and

motivation of low maintenance cost.

The rest of the paper is structured as follows:

Section 2 presents the methodology of rule-based

decision making, including fault detection,

identification, quantification, and RUL estimation

submodules. Section 3 provides the result of each

module to obtain corresponding information and how

fuzzy rules bind those information into decision and

Section 4 draws the concluded remarks.

2 METHODOLOGY

Integral to electromechanical systems, ball bearings

play a vital role, but their malfunctions can

dramatically impact the operational lifespan of

industrial processes. Through continuous monitoring

of bearing, potential faults are detected early, leading

to prompt rule-based decision. In systems demanding

high reliability and safety, timely maintenance

decisions are of paramount importance.

The functional flowchart of rule-based decision

making consists of five submodules named fault

detection, fault identification, fault quantification and

fault prognostic and use of fuzzy rule as shown in

Figure 2. These submodules are divided into

diagnostic and prognostic categories based on timing.

The diagnostic aspect includes fault detection,

identification, and quantification, while the

prognostic aspect estimates remaining useful life

(RUL). After fault detection, instantly the three other

fault analysis modules make some information’s. The

fault qualification module notes the degradation

index, the fault identification module tries to find out

which part of the bearing is showing fault traces and

the prognostic module roughly estimate the RUL.

After gathering the prerequisites information, the rule

module uses fuzzy logic to get decision.

Researchers lack a unanimous agreement on the

most suitable vibration-based fault detection, fault

ICAART 2024 - 16th International Conference on Agents and Artiﬁcial Intelligence

1246

identification, damage indicator and fault prognostic

method or parameter. While vibration feature-based

methods are simple and easily applicable, they are not

computationally intensive; Moreover, they provide

approximate quantitative damage assessment. We

utilize the Naive Bayes classifier for fault detection,

relying on time intervals between vibration envelope

peaks for fault identification. Additionally, the

capability index derived from the kernel density plot

of vibration data serves as a degradation indicator for

fault quantification. For fault prognostics, we employ

polynomial regression to predict RUL.

Figure 2: Functional flowchart of procedural decision

making in bearing after fault detection.

2.1 Fault Detection

Fault detection aims to spot deviations from normal

behavior that might result in errors, failures, or

malfunctions. It enables operators to identify

mechanical anomalies and determine the underlying

problem for subsequent targeted repairs. Common

bearing failure modes exhibit distinct characteristics,

necessitating varied identification strategies. Single

features are insufficient for precise fault detection,

prompting the use of machine learning-based fault

detection module for intelligent analysis. The fault

detection process steps are shown in Figure 3.

Noise is a natural part of vibration data, to

minimize effect of noise data we applied sliding

window with 5 points. After denoising, thirteen-time

domain features (Maximum, Minimum, Average

absolute value, Peak to peak, Variance, Standard

Deviation, Root mean square, Crest factor, Clearance

Factor, Impulse factor, Skewness, Kurtosis, and

Shape factor) are extracted from both accelerometer

data. Detailed definitions, physical meanings, and

statistical equations are described on (Wang et al.,

2019). Pearsons’s correlation coefficient is used to

select useful feature’s fault detection (Cai et al.,

2018). Maximum value, Variance, Skewness, and

Kurtosis features are picked as promising features for

fault representation.

Since the datasets contain the measurement until

the either of the accelerometer overpassed 20g, there

is no pre-defined label. We labelled the fault based on

anomalies. Anomalies, unique patterns with distinct

attributes from normal instances, hold significance

across domains, providing actionable insights. We

employ the Isolation Forest algorithm for anomaly

finding, leveraging its fast tree-based methodology

that assigns anomaly scores via binary search tree

paths (Liu et al., 2008). This algorithm

accommodates multiple features, enhancing labelling

accuracy, and draws insights from diverse monitoring

methods (Hayes & Capretz, 2014). Particularly

effective for high-dimensional problems with

irrelevant attributes or scarce anomalies, Isolation

Forest's computational efficiency suits streaming data

scenarios.

Figure 3: Flowchart of fault detection module.

After labelling, we used the Naive Bayes classifier

for fault detection, known for its efficacy in text

classification, medical diagnosis, predictive

maintenance, and fault detection (Rish, 2001). This

classifier simplifies learning by assuming feature

independence within classes. It employs Bayesian

theory, as shown in equation (1)

𝑃



𝐷



𝑥



𝑃



𝐷





𝑃



𝑥



𝑃



𝑥

𝐷





𝑃



𝐷





𝑃



𝑥



𝑃









𝑥



𝐷





(1)

2.2 Fault Identification

Fault identification is the process of specifically

pinpointing the component of location underlying

Rule-Based Decision Making in Biologically Inspired Condition Management System

1247

causes or mechanism of abnormal behavior in an

equipment. Bearing faults can be grouped into two

categories single-point defects and generalized

roughness. To vibration signature produced by

generalized roughness are vague, while the single-

point defects can be swiftly and easily identified from

the vibration signal envelope. Defects at different

components (outer race, inner race, ball, and cage)

creates different characteristic frequencies. These

defects typically produce periodic vibration signals

with repetitive patterns over time (Song et al., 2018).

This pattern facilitates frequency measurement,

contributing to the detection of periodicity or

regularity within the vibration signal. In this context,

'zero crossing time' is synonymous with the time

interval that highlights defects on bearing

components in time-domain vibration signatures

(William & Hoffman, 2011). The comprehensive

methodology for extracting vibration signatures from

time-domain vibration features is elaborated in our

earlier work (Singh et al., n.d.).

2.3 Fault Quantification

Fault Quantification finds the level of abnormality to

quantify the degree of degradation and fault (partial

or complete) (Kandukuri et al., 2016). As highlighted

before, fault quantification significantly influences

the informational value for rule-based decision-

making, and the choice of degradation indicator

features is context-specific, as no single feature

proves universally effective for all structures and

damage types. The selection of degradation indicators

(

𝐷



) involves a compromise between damage

sensitivity and alignment with anticipated structural

response. To address uncertain parameters, different

uncertainty quantification methods have emerged,

categorized into Probabilistic and Possibilistic

approaches. Probabilistic approaches treat model

input parameters as random variables with known

probability density functions (Kong et al., 2017).

Capability index have been used in the

manufacturing industry to provide quantitative

measures on process potential and performance,

moreover it can be used as damage index by fault.

Here we use kernel density function to measure the

uncertainty in the fault model, which is an extension

of the histogram. It is coined by statistician and is

expressed with different notion as process capability

index (Pearn & Chen, 1999), Six Sigma method

(Gupta et al., 2018; Kulkarni et al., 2023) Six sigma

method was developed to minimize process variance

and to improve the quality and performance of the

system. The process capability indices reflect the

degree of the process variation with respect to the

specific limits. As the degree of fault increases the

value of degradation index falls.

𝐷



𝑑−

𝜇−𝑀

3𝜎

𝑚𝑖𝑛



𝑈𝑆𝐿 − 𝜇,𝜇 − 𝐿𝑆𝐿



3𝜎

(2)

2.4 Fault Prognostics

Fault prognostics predicts future system behavior

using current condition and data history. Due to the

uncertainty and nonlinearity of the predictive models

when damage accumulates, an alternative goal is to

estimate the RUL that the system can perform in a

safe status under the future loading before one no

longer has confidence in the prognosis model. Here,

the fault prognosis module predicts the RUL of the

bearing from the time of anomaly detection timeline.

Regression is a powerful statistical method and

used widely for RUL prediction in prognosis (Kwon

et al., 2019). We've observed a non-linear relationship

between vibration feature (maximum vibration value)

and failure; therefore, we use polynomial regression.

It's important to note that the size and distribution of

the dataset used for model building impact the

regression performance of the prognostic module. We

use the 200 sample measurements for calculation of

polynomial coefficients. The mathematical

representation of our model follows equation (3),

where 'n' represents the polynomial degree and

𝑃



𝑃



.. 𝑃



are the coefficient of polynomial curve.

𝑃



𝑥



=𝑃



𝑥



+𝑃



𝑥



+⋯……+𝑃



𝑥



(3)

2.5 Fuzzy Rules

By drawing upon past experiences and knowledge,

individuals can make decisions based on the

recognition of relevant cues and the associated

outcomes associated with them. It relies on the

recognition of familiar patterns and the subsequent

application of established strategies that have proven

successful in similar contexts. The phenomenon of

decision making from the viewpoint of computer

science and information technology was presented in

(Marko Bohanec, 2009). To address uncertainty in

FMECA for CNC machine tools' manufacturing

stage, fuzzy mathematics and data envelopment

analysis are used to determine risk factors, assess

failure modes, and calculate a new RPN (Yu et al.,

2022).

Here we use Fuzzy rules to obtain decision from

the information obtained from diagnostic and

prognostic fault analysis submodules. Fuzzy rule

ICAART 2024 - 16th International Conference on Agents and Artiﬁcial Intelligence

1248

emulates human-like reasoning in handling

uncertainty and imprecision, making it ideal for

ambiguous scenarios. Fuzzy rules, structured as 'IF

Antecedent THEN Consequent,' work with linguistic

variables and smooth transitions via membership

functions. They enable flexible decision-making in

imprecise situations, bridging the gap between vague

input and actionable output. Fuzzy Rule-Based

Systems offer accountability when input data is

fuzzified (Trillo et al., 2020). These rules manage

uncertainty using degrees of truth, encoding expert

knowledge in a human-understandable format.

All input and output membership functions, fuzzy

rules and the lookup table are developed prior to the

implementation of the fuzzy logic technique to find

the rule-based decision, therefore it is less time

consuming.

In data science, defining membership functions in

fuzzy systems is crucial. Various methods exist to

describe membership functions, including horizontal,

vertical, pairwise-comparison, problem-specific,

fuzzy clustering, artificial neural networks, and

genetic algorithms (Klir & Yuan, 1995). In this

context, we opt for a simple and quick approach based

on the information range obtained from each module.

Decision variables include anomaly detection

time, damage index, and RUL. Detection time

impacts failure risk: early detection provides time to

avoid immediate failure. The damage index quantifies

fault severity inversely, with a higher index indicating

better performance. RUL guides decisions: a low

RUL suggests potential need for immediate action or

reduced operating attributes to prolong lifetime.

Decision is taken as output membership variables.

The American Roller Bearing Company's catalog,

drawing on a century of experience, provides domain

knowledge and expert opinions for defining input and

output variables as membership functions (American

Roller Bearing, n.d.). They emphasize the

temperature's influence on bearing life due to reduced

hardness at high temperatures, impacting static and

dynamic capacities. To compute actual bearing rating

life (L), one must account for speed, load, and

temperature, all inversely related to rating life (see

equation 4). where D is dynamic load rating, P is

applied load, e equals 10/3 for rolling element, R is

the rotation.

𝑅𝑎𝑡𝑖𝑛𝑔 𝑙𝑖𝑓𝑒



𝐿





𝐷

𝑃





∗ 10



60 ∗ 𝑅

∗𝑇𝑒𝑚𝑝

𝑓

𝑎𝑐𝑡𝑜𝑟

(4)

Bearings typically operate under varying loads

and speeds, defined by a duty cycle that specifies the

load, speed, and percentage of time. In these cases, a

full duty cycle occurs within one bearing revolution.

Equation 5 provides the formula for calculating the

rating life under such variable operating conditions.

where

𝑇



, 𝑇



, 𝑇



are percentage of time at different

conditions (𝑇



+ 𝑇



+ … 𝑇



= 1) and 𝐿



, 𝐿



, 𝐿



are

life in hours for each period of constant load and

speed.

𝑅𝑎𝑡𝑖𝑛𝑔 𝑙𝑖𝑓𝑒



𝐿



𝑇



𝐿



𝑇



𝐿



+⋯.. +

𝑇



𝐿



(5)

Our goal isn't to achieve the optimal decision;

instead, we aim to demonstrate experimental

validation of rule-based decision-making. Our criteria

for failure are subjective, prioritizing safe operation.

Drawing from equations 4 and 5, we consider five

procedural decision alternatives in this work based on

criticality. Load's greater quantitative impact than

speed determines their order: 'run as is' (decision

one), 'reduce speed' (decision two), 'reduce load'

(decision three), 'reduce load and speed' (decision

four), and 'stop' (decision five). These decisions

correspond to five classes: run, reduce speed (RS),

reduce load (RL), reduce load & speed (RLS), and

stop immediately. The details of membership

function, range of information variables, fuzzy sets,

and boundary of trapezoid is presented in Table 1.

Table 1: Membership functions and their boundary.

Membershi

p function

Rang

Fuzzy sets Trapezoid

Detection

Time

25000

Early

Awhile

Late

[0,0,2000,5000]

[2000,5000,

10000, 15000]

[10000, 15000,

25000, 25000]

RUL 0-

25000

Short

Intermediat

Long

[0,0,2000,5000]

[2000,5000,

10000, 15000]

[10000, 15000,

25000, 25000]

Degradation

index

0 - 21 Low

Medium

High

[0,0,1, 3]

[1,3,5,10]

[5,7, 21, 21]

Decision 1 - 10 Run

RLS

Stop

[0,0,1, 2]

[1,2,3,4]

[3,4,6,7]

[6,7,8,9]

[8,9,10,10]

Detection time, RUL and degradation indices are

taken as Antecedent, and decision is taken as

Consequent. Using a 27 linguistic rule base, derived

from empirical knowledge, and illustrated in Figure

4, rules are determined for instance:

Rule-Based Decision Making in Biologically Inspired Condition Management System

1249

RULE 1: IF early detection time AND short RUL

AND low degradation index, THEN decision is

'Stop.'

RULE 27: IF late detection time AND long RUL

AND high degradation index, THEN decision is

'Reduce speed.'"

In defuzzification, we convert the output fuzzy set

into a single crisp value. In this model, we use the

Centre of Gravity (centroid) method to calculate this

crisp output value from the accumulated membership

functions. This work utilizes the scikit-fuzzy Python

package for fuzzy logic operations.

Figure 4: Decision matrices by considering detection time

and RUL at three different stages of degradation index. The

bottom right graph shows the membership function with

five possible categorical.

2.6 Data Sets

We evaluate the model using the PRONOSTIA

datasets provided by the FEMTOST Institute,

specifically designed for accelerated degradation

tests of bearings. Our focus is on vibration data due

to its valuable insights for condition management.

These datasets include readings from two

accelerometers, sampled every 10 seconds at a

frequency of 25.6 kHz. Seventeen experiments were

conducted under three different operating conditions,

as summarized in Table 2. Six experiments

(Bearing1_1, Bearing1_2, Bearing2_1, Bearing2_2,

Bearing3_1, Bearing3_2) are utilized for training,

while the remaining eleven experiments are used for

testing. We specifically employ the 'run to failure'

experiment from the training and test datasets for

verification purposes. Figure 5 illustrates the bearing

experimentation platform, and more detailed dataset

information can be found in (Nectoux et al., 2012).

These datasets are openly available to support

prognostics research for condition monitoring,

encompassing vibration signals collected throughout

the entire lifetime from accelerated degradation tests

of rolling element bearings.

Figure 5: Overview of the experimentation platform

(Nectoux et al., 2012).

Table 2: Operating conditions of various experiments.

Operating Conditions

Condition 1 Condition 2 Condition 3

Load

(Newton) /

Speed

(RPM)

4000 / 1800 4200 /1650 5000 / 1500

Training

sets

Bearing1_1

Bearing1_2

Bearing2_1

Bearing2_2

Bearing3_1

Bearing3_2

Testing sets

Bearing1_3

Bearing1_4

Bearing1_5

Bearing1_6

Bearing1_7

Bearing2_3

Bearing2_4

Bearing2_5

Bearing2_6

Bearing2_7

Bearing3_3

3 RESULTS

In the older brain, decision-making processes tend to

be straightforward and satisfying, enabling quick

responses to potential threats or opportunities. To

elucidate the implementation process and the

practicality of the proposed decision-making

framework, we have structured the results steps in a

manner consistent with the methodology. We selected

the Bearing1_3 experiment as a case study to

illustrate the workings of each information-producing

module. The subsequent timeline and details are

elaborated upon in the methodology section.

3.1 Fault Detection

To establish a clear decision boundary between

normal and anomaly, we evaluate two parameters:

'anomaly' and 'decision score' for categorized

anomalies. If both parameters meet our criteria, we

ICAART 2024 - 16th International Conference on Agents and Artiﬁcial Intelligence

1250

label it as an anomaly. Furthermore, to enhance

confidence and reduce false positives, we consider

three consecutive timestamps with anomaly vibration

features as the detection time for anomalies. The two

top graphs in Figure 6. show the raw vibration data

from horizontal and vertical accelerometer and the

last graph shows the fault detection time (16430 sec

or 4.56 Hours) is the instance to take procedural

decision. This module results first information about

detection time. The fault detection times for all eleven

experiments are documented in the second column of

Table 3. After detecting faults, the other three module

(fault identification, fault quantification and fault

prognostic) begins to find other information.

In Bearing2_5 experiments, we observed initial

jerks and heightened vibrations that later stabilized

into smooth operation. To eliminate spurious

detections, we disregard faults occurring before 10

percent of the useful life. Any faults detected after

this point are considered genuine, and we employ

analytical methods for rule-based decision-making.

Figure 6: The top two graphs show the temporal vibration

signal of horizontal and vertical accelerometers. The last

graph shows the fault detection time at 16430 sec.

3.2 Fault Identification

The spacing between the peaks of the vibration

envelope serves as an indicator for identifying the

failure component. When the peak spacing falls

within a ±5% deviation from theoretical values, we

classify it as the identified faulty part of the bearing.

For an operating condition of 1800 RPM, the

theoretical peak spacings are 4.53 ms for inner race

defects, 5.91 ms for outer race defects, 9.28 ms for

rolling ball defects, and 76.80 ms or 58.89 ms for cage

defects. After allowing for a 5% margin of deviation,

we consider values within this range as matched cases

for fault identification.

However, in the case of Bearing1_3, as shown in

Figure 7, we cannot find peak spacings that match the

specified fault components. Out of the eleven

experiments, we can only identify the faulty part in

four experiments, as presented in the third column of

Table 3. Additionally, since we lack information

about the effects of each part's failure, we do not

incorporate this information into the rule for decision-

making process. At this moment, advanced signature

extraction techniques are essential for improving fault

detection, with a focus on future work.

Figure 7: Envelope of vibration data after fault detection (at

16430 sec).

3.3 Fault Quantification

The degradation index is computed based on

vibration samples obtained after detecting a fault. The

upper and lower specification limits for acceleration

values are set at -20g and 20g. To determine the

degradation index, we calculate both the lower and

upper limits and then select the minimum value

between these limits for each sensor. The mean

degradation index is subsequently derived by

averaging the results from horizontal and vertical

accelerometers vibration data. The quantification of

fault across all eleven experiments is presented in the

fourth column of Table 3. In case of Bearing1_3

experiment, the degradation index value is 3.36,

which is marginally acceptable for run of down

regulation.

3.4 Fault Prognostics

To expedite RUL estimation, we built a prognostic

module using a dataset of 200 samples, with 190

collected before fault detection and 10 after. We

employed third-order polynomial regression to

estimate RUL. The initial step involves extracting

maximum vibration features from both

accelerometers. Using these features, polynomial

regression models determine coefficients to map the

nonlinear relationship between these features and

time. RUL is calculated as the time difference

between fault detection and the moment the timeline

reaches the 20g failure threshold.

Figure 8 displays RUL estimates from the

prognostic model. The top graph illustrates two

maximum vibration features over time: the red line

represents horizontal max values, and the green line

Rule-Based Decision Making in Biologically Inspired Condition Management System

1251

represents vertical max values. The black vertical line

indicates the fault detection time. Once anomalies are

detected, the model uses 200 samples to calculate and

fit a regression curve. In the lower graph, the yellow

curve represents predictions from the horizontal

accelerometer, while the blue curve represents

predictions from the vertical accelerometer. The

predicted RUL for each experiment is determined by

whichever curve first surpasses the 20g amplitude

threshold, akin to the failure criteria. The predicted

RUL of eleven experiments is presented in the last

column of Table 3.

Figure 8: Estimation of RUL by curve fitting after detection

of fault by using 200 sample near the fault detection time.

Table 3: Information obtained from different fault analysis

submodules for rule-based decision making.

Experim

ents

Fault analysis

Experim

ents

Detect

ion

time

(sec)

Fault type Degr

adati

index

RUL

(sec)

B1_3

B1_4

B1_5

B1_6

B1_7

B2_3

B2_4

B2_5

B2_6

B2_7

B3_3

16430

10900

24510

16360

22140

2580

3440

4030

6890

2240

4250

Unidentified

Inner race

Outer race

Unidentified

Outer race

Unidentified

Outer race

3.36

5.63

4.58

9.48

6.38

13.28

20.1

19.32

5.5

7.68

4.88

4920

1060

580

910

830

1370

6390

2970

1150

500

1470

3.5 Fuzzy Rules

Decision making can be regarded as the analytical

processes of making a choice among several

alternatives and committing to a future course of

actions or an opinion of choice. After diagnostics and

prognostics offer crucial information, it becomes

essential to have a decision-making methodology in

place. This methodology is necessary to efficiently

make use of the available information to produce

satisfactory decisions.

We established rules to link procedural decisions

with variables, including detection time, predicted

RUL, and degradation index, drawing from

information as stated in Section 2.5. Figure 9. shows

the input and output membership function of

Bearing1_3 for decision making. Here fault detection

time is 16430 seconds, Predicted RUL from that

instance is 4920 seconds and the degradation index is

3.36. By inputting these information’s, the defined

rule gives 7.52 as defuzzification value. Based on

mapping to decision sets, it belongs to reduce load

and speed for safety critical system. Similarly, the

corresponding defuzzification values for eleven

experiments is shown in the last column of Table 4.

Figure 9: Membership functions of inputs and outputs for

the decision making. The top left shows the fault detection

time, the bottom left shows the degradation index, the top

right shows the RUL and the bottom right graph shows the

defuzzification values procedural decisions.

Table 4: Rule-based decision after defuzzification.

Experim

ent

Input membership function

Output

member

function

D_time RUL Degrad_

index

Decision

B1_3

B1_4

B1_5

B1_6

B1_7

B2_3

B2_4

B2_5

B2_6

B2_7

16430

10900

24510

16360

22140

2580

3440

4030

6890

2240

4250

4920

1060

580

910

830

1370

6390

2970

1150

500

1470

3.36

5.63

4.58

9.48

6.38

13.28

20.1

19.32

5.5

7.68

4.88

7.52

6.69

9.17

7.59

8.13

3.21

1.84

3.93

6.68

3.86

6.61

As mentioned previously, our aim is not to

identify the best decision but to experimentally

ICAART 2024 - 16th International Conference on Agents and Artiﬁcial Intelligence

1252

validate rule-based decision-making. However, these

decisions are subject to the failure criteria outlined by

the data provider. The five decision alternatives

include: running as is, reducing speed, reducing load,

reducing load and speed, and stopping.

In this scenario, we assume the decision

membership function ranges from zero to 10 after

defuzzification. We divide this range equally into five

categories. For instance, decision values from 0-2 are

associated with 'run as is,' 2-4 with 'reduce speed,' and

so forth.

Out of the eleven experiments conducted,

Bearing2_4 aligns with the 'run as is' decision, while

Bearing2_3, Bearing2_5, and Bearing2_7 correlate

with 'reduce speed.' Similarly, Bearing1_3,

Bearing1_4, Bearing1_6, Bearing2_6, and

Bearing3_3 fall under 'reduce load and speed,' and

Bearing1_5 and Bearing1_7 signify the 'immediate

stop' decision. Overall, these decisions seem

promising in preventing catastrophic failures in

safety-critical systems and in prolonging bearing life

through downregulation.

4 CONCLUSION

We apply rapid decision-making techniques inspired

by the old brain to enhance timely and effective

decision-making for time-sensitive industrial

equipment. This approach bridges the gap between

cognitive science and condition monitoring, offering

a broader perspective on sustainability and inspiring

future research in design, modelling, validation, and

human-in-the-loop concepts.

In bearing maintenance and fault diagnosis, swift

decisions are crucial to avert catastrophic failures and

minimize downtime. Our model, utilizing vibration

data and extract various information (detection time,

RUL and degradation index , and use that information

with established rules to make instant decisions.

In scenarios with sparse data and basic algorithms,

this rapid decision-making approach proves

beneficial for condition management. It's anticipated

to assist maintenance engineers in enhancing bearing

inspection programs' efficiency. With a specific focus

on bearing faults, these findings offer promise for

real-world applications.

Future work involves experimental validation of

advanced cognitive processes, like root cause

analysis, integrated with procedural decision-making

for improved maintenance actions. This may also

include the incorporation of deep learning and

optimization techniques to continuously enhance

maintenance strategies.

ACKNOWLEDGEMENTS

The authors thank to Maneesh Singh, Knut Øvsthus

and Anne-Lena Kampen for providing useful

discussion.

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