Reducing Data Uncertainties: Fuzzy Real-Time Safety Level Methodology for Socio-Technical Systems

Comment Description

Response

Reviewer 1

In the manuscript, the authors introduced Fuzzy Real Time Safety Level methodology which is used in an automated solar park system that rotates its photovoltaic panels. The manuscript is well-structured and well-written. The topic is interesting and worth investigating, especially given the authors' statement “Using this array configuration and PV panel alignment system, the park is able to produce 28-30% more electrical energy than if the panels were stationary”. Even though the article is interesting in its current format, some aspects should be improved for possible publication and for a better understanding by the readers.  

My remarks are as follows:

We thank the reviewer for their positive feedback and for finding our research interesting and relevant. We appreciate their comments on the structure and clarity of the manuscript. We also acknowledge the importance of the improvements they suggested to enhance the paper's quality and to ensure that it is more accessible to the readers.

We will carefully address the points raised and make the necessary revisions to strengthen the manuscript.

1. In the abstract and introduction sections, the novelty and the authors' contributions should be better clarified.

We thank the reviewer for their comment.

The abstract was heavily modified within the 200-word limit. The most notable addition was the final sentence:

“Knowing the safety level of a system in real-time is crucial for the systems in question as it enables proactive risk management and enhances decision-making by providing immediate insights into potential hazards, safeguarding against accidents.”

Additionally in the Introduction section the following paragraphs were added in addition to minor changes in the section:

“Introducing the capability of knowing what the safety level of a system is during its operation is crucial for any Industry operating complex systems as it can enable proactive risk management, ensure operational stability and enhance decision-making in terms of safety by providing immediate insight into potential hazards and future accidents.”

“This manuscript incorporates the information uncertainties of complex systems by implementing fuzzy logic in the RealTSL methodology. Fuzzy logic is used in order to increase the applicability of the methodology (RealTSL) in systems where it is not possible or is impractical to have sensory information about every potential unsafe system state the system could be in during its operation. Triangular fuzzy numbers are used to represent the estimated time remaining until an accident can be caused by a sequence of unsafe system states incorporating the degree of confidence toward the sensory information that are available about those unsafe system states. Then an ordering of fuzzy numbers is used to determine the most detrimental to safety sequence of unsafe system states.”

2. A related work section is missing.

Taking into consideration the reviewer’s comment the related work paragraphs of the introduction section were moved to a separate section in accordance with this comment.

3. The case study section should be described in more detail.

We are not sure how to embody this comment in our paper. Could the reviewer be more specific on what part of the case study should be described in more detail?

4. Please give a strengths and weaknesses of the proposed model.

We thank the reviewer for pointing this out, we added the following paragraphs in the conclusions section:

“Strengths of the proposed fuzzy RealTSL are the greatly increased applicability of the methodology when compared to its non-fuzzy counterpart. Since, a wider range of systems can be analysed using this methodology the ability to know what their safety level is can enable proactive risk management and enhance decision-making potentially averting future accidents that could lead to losses of life, injuries, damages to equipment and other financial losses. Losses of the proposed methodology include an increased workload to produce an analysis using the methodology as additional parameters need to be deter-mined as well as monitored during operation. This could cause also an increase in the data that needs to be processed in order to achieve a calculation of the safety level, this could increase the computational power needed to produce a calculation of the safety level in real-time in more complex systems. The effect of the fuzzy RealTSL on the accuracy of the calculation is hard to determine when comparing to the non-fuzzy version as the non-fuzzy version can’t be used in as wide a range of potential systems. This effect should be studied more in the future to determine potentially a metric for how many sensors should be in place to maximise accuracy of the calculations while minimizing the cost of introducing additional sensors in the system.”

Technical remarks:

1. Line 130: is (Dokas, et al., 2013), should be [4].

We thank the reviewer for pointing this out, it was  corrected exactly as noted.

2. All formulas should be checked.

We removed the explanations of parameters “where …” from formula (2), (8) and added them as normal text after the formulas. Made an additional correction to formula (1).

3. Table 1: L-2 is needed in the first column.

We thank the reviewer for pointing this out, it was  corrected exactly as noted.

Comment Description

Response

Reviewer 2

The paper presents the concept of reducing data uncertainty in Socio-Technical Systems using the fuzzy logic theory. An automatic photovoltaic (PV) panel alignment system is presented as a case study. General remarks: the title, abstract, keywords, and introduction are clear and sufficient. However, I have several questions and suggestions related to the manuscript contents that should be addressed before considering for publication:

We thank the reviewer for their thoughtful review and for recognizing the clarity of the title, abstract, keywords, and introduction. We appreciate the interest in our work and their valuable feedback.

We acknowledge the need to address the questions and suggestions raised to further improve the manuscript.

1. Can you clearly explain what advantages the fuzzy logic approach offers instead of the traditional 2-value logic in the system under consideration?

The non-fuzzy version of the RealTSL methodology is unable to be applied to the Panel Alignment System due to the lack of awareness constraints (sensors) as noted in section 5.5 makes it impossible to calculate any safety level because there would be no information about the existence of potential unsafe system states in the system.

2. What were the criteria for selecting the fuzzy set types (there are triangular sets shown as examples in section 3.2)?

Indeed, this was an oversite on our part. We added the two following paragraphs in section 4.2 (previous 3.2):

“While a trapezoidal fuzzy number was also considered the triangular variant was finally chosen because conceptually it fit the idea of the uncertainty of the available information because the peak represents the ideal or most certain value, while the slopes capture the gradual decrease in certainty as you move away from this value. In addition, the triangular set consist of straight lines, their calculation involves only basic linear interpolation. This leads to reduced computational overload compared to more complex shapes like Gaussian or sigmoidal fuzzy sets. Since the point of RealTSL is to conduct real-time calculations, resources are limited, and triangular sets are ideal for that.”

“The authors investigated multiple potential ordering relations for the fuzzy numbers in order to identify one that fit the requirements of this approach. These requirements were firstly in case the “spread” of the triangular numbers was 0 to be able to order them numerically, meaning that t_1= 5 sec < t_2= 10 sec. Secondly, because in this case the smaller the time value for time remaining until the accident the more “severe” that path is toward safety. Then the larger the base of the triangle meaning the more uncertain that value of the time remaining until accident is it should also be smaller so that it is more “severe” toward safety. Furthermore, many of the ordering relations we found were meant for comparing pairs of fuzzy numbers whereas the CPS ordering utilised the number CPS(A_i ) that makes it easier to order multiple fuzzy numbers. Finaly since this methodology is meant to operate in real-time the simple calculations required to conduct the CPS ordering would not drastically increase the computational needs to conduct the new fuzzy RealTSL when compared to alternative options.” 

3. How were the fuzzy sets identified and tuned to meet the specific task of managing a solar park?

The fuzzy sets determination of the case study is presented in section 5.5 (previous 4.5) formula (15) & (25).

4. A fuzzy inferencing system is not presented, and no fuzzy rules are formulated

.

Yes indeed. Fuzzy numbers are used to represent the time remaining until the accident parameter and are the ordered according to CPS ranking in order to identify which path is the most detrimental to safety. 

5. Editorial: many "Error! Reference source not found" warnings in the introduction section.

We thank the reviewer for pointing this out, the errors were replaced with the correct reference.

6. The conclusions should clearly summarise the strengths and limitations of the proposed model. Please update.

We thank the reviewer for pointing this out, we added the following paragraphs in the conclusions section:

“Strengths of the proposed fuzzy RealTSL are the greatly increased applicability of the methodology when compared to its non-fuzzy counterpart. Since, a wider range of systems can be analysed using this methodology the ability to know what their safety level is can enable proactive risk management and enhance decision-making potentially averting future accidents that could lead to losses of life, injuries, damages to equipment and other financial losses. Losses of the proposed methodology include an increased workload to produce an analysis using the methodology as additional parameters need to be deter-mined as well as monitored during operation. This could cause also an increase in the data that needs to be processed in order to achieve a calculation of the safety level, this could increase the computational power needed to produce a calculation of the safety level in real-time in more complex systems. The effect of the fuzzy RealTSL on the accuracy of the calculation is hard to determine when comparing to the non-fuzzy version as the non-fuzzy version can’t be used in as wide a range of potential systems. This effect should be studied more in the future to determine potentially a metric for how many sensors should be in place to maximise accuracy of the calculations while minimizing the cost of introducing additional sensors in the system.”

Comment Description

Response

Reviewer 3

1.        Please provide a comprehensive explanation of the fuzzy logic methods employed in the methodology, including the specific algorithms and parameters used.

We thank the reviewer for their comment we tried to address their concerns by adding the two following paragraphs in section 4.2 (previous 3.2):

“While a trapezoidal fuzzy number was also considered the triangular variant was finally chosen because conceptually it fit the idea of the uncertainty of the available information because the peak represents the ideal or most certain value, while the slopes capture the gradual decrease in certainty as you move away from this value. In addition, the triangular set consist of straight lines, their calculation involves only basic linear interpolation. This leads to reduced computational overload compared to more complex shapes like Gaussian or sigmoidal fuzzy sets. Since the point of RealTSL is to conduct real-time calculations, resources are limited, and triangular sets are ideal for that.”

“The authors investigated multiple potential ordering relations for the fuzzy numbers in order to identify one that fit the requirements of this approach. These requirements were firstly in case the “spread” of the triangular numbers was 0 to be able to order them numerically, meaning that t_1= 5 sec < t_2= 10 sec. Secondly, because in this case the smaller the time value for time remaining until the accident the more “severe” that path is toward safety. Then the larger the base of the triangle meaning the more uncertain that value of the time remaining until accident is it should also be smaller so that it is more “severe” toward safety. Furthermore, many of the ordering relations we found were meant for comparing pairs of fuzzy numbers whereas the CPS ordering utilised the number CPS(A_i ) that makes it easier to order multiple fuzzy numbers. Finaly since this methodology is meant to operate in real-time the simple calculations required to conduct the CPS ordering would not drastically increase the computational needs to conduct the new fuzzy RealTSL when compared to alternative options.” 

2.        Clearly explain how the proposed methodology addresses the limitations of traditional Real-Time Simulation and Learning (RealTSL) approaches, providing relevant examples to illustrate these improvements.

We thank the reviewer for their comment.

In the Introduction section the following paragraphs were added in addition to minor changes in the section:

“Introducing the capability of knowing what the safety level of a system is during its operation is crucial for any Industry operating complex systems as it can enable proactive risk management, ensure operational stability and enhance decision-making in terms of safety by providing immediate insight into potential hazards and future accidents.”

“This manuscript incorporates the information uncertainties of complex systems by implementing fuzzy logic in the RealTSL methodology. Fuzzy logic is used in order to increase the applicability of the methodology (RealTSL) in systems where it is not possible or is impractical to have sensory information about every potential unsafe system state the system could be in during its operation. Triangular fuzzy numbers are used to represent the estimated time remaining until an accident can be caused by a sequence of unsafe system states incorporating the degree of confidence toward the sensory information that are available about those unsafe system states. Then an ordering of fuzzy numbers is used to determine the most detrimental to safety sequence of unsafe system states.”

3.        What is the main contribution of this paper? Please also add some directions for future research.

We thank the reviewer for their comment.

The abstract was heavily modified within the 200-word limit. The most notable addition was the final sentence:

“Knowing the safety level of a system in real-time is crucial for the systems in question as it enables proactive risk management and enhances decision-making by providing immediate insights into potential hazards, safeguarding against accidents.”

Additionally in the Introduction section the following paragraphs were added in addition to minor changes in the section:

“Introducing the capability of knowing what the safety level of a system is during its operation is crucial for any Industry operating complex systems as it can enable proactive risk management, ensure operational stability and enhance decision-making in terms of safety by providing immediate insight into potential hazards and future accidents.”

“This manuscript incorporates the information uncertainties of complex systems by implementing fuzzy logic in the RealTSL methodology. Fuzzy logic is used in order to in-crease the applicability of the methodology (RealTSL) in systems where it is not possible or is impractical to have sensory information about every potential unsafe system state the system could be in during its operation. Triangular fuzzy numbers are used to represent the estimated time remaining until an accident can be caused by a sequence of unsafe system states incorporating the degree of confidence toward the sensory information that are available about those unsafe system states. Then an ordering of fuzzy numbers is used to determine the most detrimental to safety sequence of unsafe system states.”

The following paragraph is provided in the conclusions in terms of future work directions:

“As future work, there is a discussion to conduct a long-term (1 to 2 years) application of the fuzzy RealTSL on the Panel Alignment System and determine the potential increase in the productivity of the system in that time, while also setting up some warning notifications to the owner in case losses of production, or accidents are expected. Potential further future work ideas are the development of a software solution that could make larger scale applications of the methodology easier by combining a database of all unsafe system states, real time recording of the appropriate information and calculation of the safety level as well as a user interface to empower system managers with information that can be generated by the methodology.”

We also added the following after addressing comments about the weaknesses of the proposed methodology:

“The effect of the fuzzy RealTSL on the accuracy of the calculation is hard to determine when comparing to the non-fuzzy version as the non-fuzzy version can’t be used in as wide a range of potential systems. This effect should be studied more in the future to determine potentially a metric for how many sensors should be in place to maximise accuracy of the calculations while minimizing the cost of introducing additional sensors in the system.”

4.        Please cite more recent references from the Safety to show the relevance of your study for the journal.

Added the following reference in the introduction section:

Asgari, A.; Beauregard, Y. Using a Brain-Inspired Decision-Making System to Model a Real-Time Responsive Risk Assessment of the Dynamic Tasks Involved with Hazardous Materials. Safety 2022, 8, 45. https://doi.org/10.3390/safety8020045

“More recently [2] have identified the need for such analyses in the case of dynamic risk due to “Dynamic environments are uncertain and their aspects are varied, not only in their intrinsic parameters but also in how those aspects are affected by externally unpredictable factors. It is worth noting that the uncertainty of an environment can present both risk and opportunity for developing systems and drive the performance of the embedded processes to provide products and services at higher rates and diversities.”.”