The Maintenance Factor as a Necessary Parameter for Sustainable Artificial Lighting in Engineering Production—A Software Approach

Abstract

The presented article addresses the issue of the maintenance factor, which forms a part of the design variables in artificial lighting within engineering practices from a sustainability perspective. The maintenance factor was monitored using two simulation tools—Dialux, version 5.12.0.5527 and Relux, version 2024.2.8.0. In a production hall, inadequate lighting was identified with a value below 300 lx, prompting a redesign of the lighting system. The overall methodology of the Ergonomic Rationalization Sequence was expanded in the “Design of Lighting System” phase to include the determination of the maintenance factor as a necessary parameter for sustainability, which was subsequently verified in a virtual environment using two options in a practical study. According to the in situ measurements, the virtual environments of the production hall were created for both software, in which four alternatives for the lighting system were developed. The illuminance values met the normative requirements in each alternative; however, the first two (illuminance values 1000 lx–1200 lx) were predicted to have long-term high-energy consumption. In alternatives 3 and 4, the number of luminaires was therefore reduced from 6 pieces to 4, with a total illuminance in the range of 680 lx–780 lx. The determination of the variations in the methods for establishing the maintenance factor identified a deviation of 5%, which, indicating the changes in illuminance values, can be considered as the occurrence of a gross error in lighting design.

1. Introduction

During the process of designing lighting systems, priority is given to setting the correct uniformity of illumination, the brightness, and, not least, the illuminance. These quantitative parameters of the lighting system are key factors for proper lighting design. However, to achieve operational efficiency, which is associated with energy savings, it is essential to continuously ensure an adequate maintenance process, which leads to the sustainability of the lighting system itself. The maintenance factor is therefore considered to be an important aspect of sustainability, as it directly relates to the efficacy of luminaires, their lifespan, and their operational reliability. The importance of the maintenance factor in interior lighting design is discussed in various research publications [1,2,3,4,5,6], although these articles are not directly linked to sustainability issues. All of these studies can, however, be extrapolated and directly linked to sustainability issues, of which one of the key factors is resource conservation and ensuring efficiency. Sustainability is implemented in various sectors, including manufacturing, research, and development. In lighting issues, the current trend is focused on examining the materials of the lighting system structures and their potential for recycling [7,8,9], as well as the design of the lighting systems ensuring easy maintenance [10,11,12], or the implementation of technologies for energy-efficient lighting [13,14,15]. Although most of the aforementioned research studies do not directly address the sustainability and maintenance factor of lighting, they are more commonly known in the context of energy management research and the adoption of green technologies [16,17]. These and many other documents describing the issue suggest that lighting systems designed for easy maintenance align with the principles of environmental sustainability. The adoption of such systems can contribute to the broader goals of reducing environmental impacts and promoting sustainable practices across all sectors [18]. The maintenance factor of lighting can be determined not only through computational methods but also by utilizing software solutions. Conventional computational methods are included in standards and guidelines, using precise values that identify the cleanliness state of the luminaire, taking into account the operational, environmental, and age characteristics affecting the degradation of illuminance, as well as the regular analysis of the impact of various parameters on the luminaires [19,20]. These methods provide a systematic approach to considering losses in illuminance over time and help in maintenance planning. However, many research questions arise regarding the complexity of the environmental impacts or the variability of the luminaire properties when correctly determining the maintenance factor [21,22]. The issue of determining the maintenance factor can also be addressed through software solutions. As early as 1999, a specific approach to addressing the deterioration of illuminance over time was noted, where Perry emphasized the need to reassess the maintenance factor based on the obtained data. This research provides partial foundations for using simulation tools in the continuous reassessment of the maintenance factor of lighting systems [23]. Later, simulation software began to be used to create virtual maintenance of the lighting system, which allows for an assessment of the efficiency of evaluating the impact of lighting on maintenance [24]. Over the years, using simulation tools for lighting design has led to discrepancies among experts regarding the accuracy of commercially used simulation tools. For instance, Skarżyński [25] states that the accuracy of the lighting simulations performed in the Dialux 4.13 program primarily depends on the user’s expertise. Gábrová et al. [26] describe differences in the results obtained from the various daylight simulation programs, indicating potential changes in accuracy for complex cases. Sun et al. [27] compared several software tools for lighting design with physical measurements, identifying variations in the accuracy of the data obtained based on the simulation parameters. These and other research studies [28,29,30,31,32] highlight the need for further research in the established area. In addition to addressing the accuracy of simulation tools, practical applications of simulation tools to improve lighting systems and thus indirectly ensure sustainability are illustrated, such as in the study by Králikova et al. [33], which points to the practical benefits of using software tools for optimizing lighting design in classrooms in terms of both maintenance and efficiency. A properly chosen simulation tool thus helps to reassess the maintenance factor, evaluate it, and define its design efficiency. Software programs are primarily used in the design phase to ensure the correctness of the implemented solution in every area. While the above articles and many others [34,35] provide valuable information on computational methods for maintenance factors, there are indications of potential gaps in the capabilities of the current solutions for proper lighting design. The simulation and optimization techniques used in contemporary software are often utilized to solve practical problems, primarily focusing on outdoor lighting [36,37] or road lighting [38,39]. This article, however, discusses the determination of the maintenance factor of lighting from a sustainability perspective, focusing on the engineering industry, and highlights the differences in design concerning monitored parameters. In response to a study conducted in the field of manufacturing practice, the article also extends the “Design of Lighting System” phase in the Ergonomic Rationalization Sequence [40]. The need to identify and correctly establish the maintenance factor of lighting is essential not only for the sustainability of the lighting system but also for ensuring occupational well-being.

2. Materials and Methods

Every lighting system that enters operation begins to degrade. This degradation is caused by various factors, such as the accumulation of dirt, the aging of light sources, or luminaires. Therefore, during the design phase of lighting systems, it is essential to determine the maintenance factor, which represents the maximum reduction in illuminance or brightness that the lighting system must compensate for to ensure that the design parameters are met at all times during the system’s intended lifespan and under specified conditions [41].

The maintenance factor accounts for the reduction in luminaire efficiency and luminous flux. This reduction is caused by dirt deposited on the luminaire’s optics or inside the light source. The extent of degradation depends on the luminaire’s surface finish, construction, degree of protection, type of light source, its placement, the environment in which it is used, and air circulation in the room. In humid and heavily contaminated areas, it is recommended to use enclosures with at least an IP54 rating. In very polluted environments, illuminance levels can drop by up to 50%, which also impacts economic efficiency. The surface finish and material of the luminaire are also important factors. The maintenance factor is expressed by a mathematical equation as outlined in the 97: 2005 Guide on the Maintenance of Indoor Electric Lighting [42]:

MF = LLMF × LSF × LMF × RSMF

(1)

where:

−

LSF—Lamp Survival Factor

−

LLMF—Lumen Lamp Maintenance Factor

−

RSMF—Room Surface Maintenance Factor

−

LMF—Luminaire’s Maintenance Factor

Lamp Survival Factor—This factor identifies the probability of the luminaire or light source remaining operational. It also considers the replacement regime for the luminaire or light source. In practice, individual replacement of sources or luminaires is most commonly applied.

Lumen Maintenance Factor—This factor expresses the gradual reduction in the light source’s luminous flux during normal operation due to the aging of the luminaire or light source. It is defined by the ratio of luminous flux at the current observed period to the initial flux of the luminaire or light source. This factor is determined for luminaires with integrated light sources.

Room Surface Maintenance Factor—This factor considers the degradation of surface reflectivity. When determining the maintenance factor in indoor environments, it must account for all light-reflecting surfaces.

Luminaire Maintenance Factor—This factor expresses the relative luminous flux emitted by the luminaire due to dirt accumulating on its optical parts or components affecting the luminous flux.

Two software tools were selected for the study, which provide the capability to calculate the maintenance factor. These tools, Dialux Evo and Relux Desktop, are most commonly used in lighting system design due to their availability, free access versions, processing and interpretation capabilities, and support for current legislation and standards for lighting assessment.

3. In Situ Measurements and Creation of Current State Models

In both software solutions, a virtual working environment of an industrial hall with dimensions (10 × 13 × 8) m was created (Figure 1).

Given the capabilities offered by both programs, a comprehensive model of the working environment was created in the simulation environment of the Dialux Evo software. This layout was then imported into the Relux software environment. Based on both technical drawings and in situ measurements, a detailed model of the working environment was constructed in Dialux. This model includes three CNC machines and auxiliary tables (AT1–AT3) used for auxiliary tasks, storage of tools, or intermediate storage for further production. The analyzed working environment was constructed in accordance with the working procedure of the simulation model [40].

To build the model, the first step involved determining the dimensional characteristics of the analyzed space and classifying the space and its application (Figure 2), which ensures compliance with normative requirements.

In the created models, the reflectances of objects and materials were set to the same values in both software tools, in accordance with the recommendations established by the ISO/CIE TS 20 012 36 0072 standard [43]. The creation of the lighting system (characteristics of luminaires and their arrangement in the space) was based on the initial in situ measurements. Due to the outdated lighting system, it was not possible to identify precise parameters, and no technical documentation existed. Initial measurements were conducted at three measurement points on the auxiliary tables (designated AT1–AT3). At each measurement point, a series of continuous measurements was performed for 5 min, with data recording intervals of 5 s. Measurements were conducted in accordance with international standards of the International Commission on Illumination using a KIMO LX200 lux meter (Kimo Electronic Pvt Ltd., Mumbai, India) and evaluated with LLX200-specialized software., v1.3.2.0. The following graph (Figure 3) shows the average values of illuminance and uniformity at points AT1, AT2, and AT3—the monitored areas of the production hall. According to current standards [44], the illuminance level for this type of operation should reach at least 300 lx, and up to 500 lx in spaces without natural light. The graph clearly shows that the illuminance levels at the monitored points do not meet these minimum values, necessitating the rationalization of the lighting system. Adjustments based on uniformity values are not needed, as all monitored points showed values above 0.6.

The necessity of rationalizing the lighting system can also be confirmed by the illuminance measurements over time, as shown in Figure 4. These time-dependent measurements revealed significant fluctuations in illuminance over short periods, primarily due to the outdated and poorly maintained lighting system.

Based on the initial measurements, a comprehensive model of the current lighting system was created (Figure 5) to facilitate the subsequent redesign. Three calculation planes were also established, which were analogous to the in situ measurements—AT1, AT2, and AT3.

The results obtained from the simulations were then verified against the in situ measurements to facilitate the light redesign in the production hall. The values obtained from the simulations and the deviations from the in situ measurements are presented in

Table 1. Comparison of in situ measurement data with the model solution of the current state.

	Lighting Parameter	AT1	AT2	AT3
In situ measurement	EavgI [lx]	274	279	283
	Uo [-]	0.91	0.94	0.93
Dialux	EavgD [lx]	278	282	288
	Uo [-]	0.91	0.94	0.93
Deviation	EavgD and EavgI [%]	1.44	1.06	1.74
Relux	EavgR [lx]	275	284	285
	Uo [-]	0.91	0.94	0.93
Deviation	EavgD and EavgI [%]	1.09	0.70	1.05

.

From the conducted comparison and its graphical interpretation (Figure 6), the accuracy of the constructed models can be confirmed. The deviations identified between the real measurements and the simulation data did not exceed 2%, which is within the acceptable percentage difference between computer simulation results and field measurements—a maximum of 15% [45]. The deviations in the results are likely due to differences in the calculation algorithms or rendering engines used by the programs. Since the same reflective surfaces were set in both programs, any acceptable deviation is primarily attributed to the differences in the calculation algorithms.

4. Extension of Digital Lighting Design Sequence to Include Maintenance Factor in Stage F

The definition of a new lighting system with the specification of the maintenance factor was carried out in accordance with [40,46], with the sequence [40] extended to address the general determination of the maintenance factor in stage F—Design of the Lighting System. The created extension is presented in the following diagram (Figure 7).

The proposed extension incorporating the maintenance factor consists of the following parts:

Selection of Maintenance Factor Method—In this part, it is essential to correctly select the method for determining the maintenance factor, as this choice influences the achieved values of the monitored lighting parameters. In simulation tools, the maintenance factor can be considered through two options: Option 1—Fixed Maintenance Factor and Option 2—Determination of Maintenance Factor According to Partial Parameters via CIE Formula (1)

In both options, the process continues similarly, specifying the lighting system and identifying the exact position of each luminaire. If the maintenance factor is established as a fixed variable in the initial part of the lighting system design, the process then returns to the main sequence, specifically to step G—Creation of the Calculation Area.

If Option 2—Determination of Maintenance Factor According to Partial Parameters via CIE Formula (1)—is selected, it is necessary to identify each parameter of the equation in conjunction with the capabilities of the software solution, as well as the available information and knowledge. For example, when determining the Lamp Lumen Maintenance Factor (LLMF), it is important to distinguish whether the lighting system has an integrated or non-integrated light source. Additionally, for systems with LED luminaires, this parameter is determined based on the interval for replacing luminaires, which generally corresponds to the median lifespan Lx. When determining the maintenance factor (MF), it is also necessary to establish the type of luminaire replacement (individual/group), which is considered in the Lamp Survival Factor (LSF). Besides the type of replacement, it is crucial to identify the degradation of surface reflectance, accounted for in the Room Surface Maintenance Factor (RSMF). This includes setting the cleaning interval for surfaces and determining the reflectance of walls, floors, and ceilings. After comprehensively identifying all components of the maintenance factor, it is possible to continue with the main sequence in step G.

5. Maintenance Factor Determination for the Industrial Lighting System via Simulation Software

In the created and verified model, a lighting system was created in both software programs in accordance with the proposed extension shown in Figure 7. In Dialux, the method for determining the maintenance factor was selected through the “Construction” tab in the “Spaces” module (Figure 8). In this module, the space in which the maintenance factor is to be determined was identified, and then the method for determining the maintenance factor was selected—either fixed (Option 1) or, according to CIE 97:2005 [47], (Option 2). When the “fixed” option is selected, the maintenance factor (MF) is automatically predicted at a level of 0.80 (the value can be manually adjusted within the range of 0—1), and it is not possible to set the interval for inspection or surrounding conditions. These parameters were set in the interface of the CIE 97:2005 method in accordance with the currently valid regulatory standard.

In Relux, the method selection was carried out through the “Calculation Manager” module (Figure 9), where the method for determining the maintenance factor—fixed (Option 1)—is labeled as “classical”, and the method according to CIE 97:2005 (Option 2) is labeled as “for each luminaire type”.

After selecting the method for determining the maintenance factor, the procedure is the same for both options. As mentioned in the previous section of the article, it is not possible to specify the exact characteristics of the existing luminaires due to their obsolescence and lack of documentation. In this case, the implementation of a simulation solution is highly desirable, as it allows for the prediction of the basic characteristics of the lighting system based on the current model. This predictive capability enables the quicker and more flexible creation of a new lighting system that meets all normative and legislative requirements.

For the creation of the new lighting system, luminaires with higher Correlated Color Temperature (CCT) and lamp flux were proposed. Studies were conducted in the model based on the current state, considering four basic alternatives (

Table 2. . Characteristics of the lighting system in the predicted current state and proposed alternatives.

	Current State	Alternative No 1	Alternative No 2	Alternative No 3	Alternative No 4
Lamp flux [lm]	10,300	35,000	35,000	35,000	35,000
Luminous efficacy [lm/W]	129	148	148	148	148
Lamp power [W]	80	230	230	230	230
CCT [K]	5000	6500	6500	6500	6500
CRI [-]	80	80	80	80	80
MF method	Option 1	Option 1	Option 2	Option 1	Option 2
No. of luminaires	6	6	6	4	4

), which differ in the method selected for determining the maintenance factor as well as the number of luminaires.

In the first two alternatives, the same number of luminaires with the original layout were retained to eliminate the costs associated with implementing a redesign. This approach involved only replacing the luminaires themselves, provided the values of the monitored parameters were sufficient, as the uniformity of illumination was satisfactory with the existing luminaire arrangement. In the third and fourth alternatives, due to the high lamp power, to achieve better energy efficiency, the total number of luminaires was set to four with automatic uniform distribution of luminaires in the production hall (Figure 10). However, it was necessary to identify not only the illuminance parameter but also the uniformity of illumination.

The individual alternatives are dimensioned for both methods of selecting the maintenance factor (MF). In Option 1, the maintenance factor was set according to the constant, predefined in both simulation programs, at a value of 0.8. In Option 2, the maintenance factor was determined as a result of the individual components of the mathematical formula (Figure 11).

The determination of the maintenance factor concluded the extension sequence, and in accordance with its components, the creation of the lighting redesign models in the production hall was completed. The measurement grids of the calculation areas were defined uniformly across the entire calculation surface, identically in both programs. In the final step, the simulation of the individual artificial lighting solution alternatives was performed. Numerical and graphical results of the examined parameters and their evaluations are presented in the following section of the article.

6. Results and Discussion

For the purposes of this study, the primary parameter monitored in Alternatives 1 and 2 was the illuminance, while in Alternatives 3 and 4 both illuminance and uniformity of illumination were assessed.

Table 3. Illuminance distribution of artificial lighting on the reference plane through isolux lines in all simulation alternatives.

Dialux	Relux
Isolux lines of artificial lighting illuminance—Alternative No. 1
Isolux lines of artificial lighting illuminance—Alternative No. 2
Isolux lines of artificial lighting illuminance—Alternative No. 3
Isolux lines of artificial lighting illuminance—Alternative No. 4

displays the illuminance distribution of the artificial lighting through the course of the isolux lines on the reference plane, providing a graphical interpretation of the most and least illuminated areas in the space.

According to the defined isolux interpretation scale, in the Dialux program, the illuminance is satisfactory if the monitored space is represented by beige (600 lx), orange (900 lx), or brown (1100 lx) colors. For the monitored spaces—AT1, AT2, and AT3—an illuminance level above 600 lx is considered satisfactory. In the Relux program, the isolux interpretation scale uses a different color scheme compared to the Dialux program, where dark green and blue shades are considered satisfactory. Within the monitored spaces, the illuminance was also deemed satisfactory.

The parameter of illuminance in all four alternatives was also assessed quantitatively. In Alternatives 1 and 2, the number and exact positions of the luminaires were retained. This design was chosen for economic efficiency, as the investment costs would involve purchasing new luminaires and the execution of the dismantling and installation of the new lighting system.

In Alternatives 1 and 2, the illuminance values exceeded the normative requirements by double (Figure 12). This result negatively impacts long-term energy consumption, making it necessary to also analyze the luminance at the specified positions (AT1, AT2, and AT3) from a photometric perspective.

For Alternative 1, the average lighting levels are 1064 lx (Dialux) and 1066 lx (Relux), while for Alternative 2, they are 1113 lx (Dialux) and 1119 lx (Relux). In both cases, a deviation of approximately 5% was observed when using different options for the maintenance factor (fixed and calculated). This indicates a risk of oversizing the lighting system when relying on a pre-set maintenance factor. Consequently, an increase in illuminance values based on an incorrect determination of the maintenance factor could negatively impact energy consumption. Over time, this could lead to unnecessary oversizing, resulting in energy waste.

In Alternatives 3 and 4, the number of luminaires was reduced, which positively affects energy consumption. However, this leads to higher initial costs associated with redesigning the electrical installation system and the new layout of the lighting system. Despite this, these alternatives also met the normative requirement of a minimum illuminance level of 500 lx (Figure 13).

By reducing the number of lamps in Alternatives 3 and 4, a lower energy demand was achieved, which can be further reduced through the accurate determination of the maintenance factor, as seen in the previous two alternatives. Even for Alternatives 3 and 4, a deviation of approximately 5% was identified when using fixed versus calculated maintenance parameters, once again highlighting the risk of oversizing the lighting system, which would increase energy consumption.

Comparing the achieved illuminance values for each alternative using the software solutions, the deviations ranged from 0.18% to 1.67%, which is negligible in practice. This confirms the correctness of the models created in both software programs and their applicability within this study.

In Alternatives 3 and 4, the uniformity of illumination was also analyzed due to the changes in the number and positions of the luminaires. The data obtained (Figure 14), shown in the following figure, indicate that the uniformity of illumination is the same at all the monitored points, specifically with a value of 0.93. Since the minimum normative requirement for the uniformity of illumination in production areas is set at 0.6 or higher, the positions of the luminaires in the proposed design can be considered suitable.

In analyzing the impact of the maintenance factor value on the overall illuminance at the analyzed locations, two methods of determination were monitored across the four alternatives. In a comprehensive comparison of Option 1 and Option 2 for both simulation tools in all alternatives, a deviation of 5% was identified for Option 1 compared to Option 2. This indicates that the fixed maintenance factor value of 0.8 provided by the programs introduces an inaccuracy that can result in a significant error in lighting design, potentially leading to a misjudgment of compliance with the established normative requirements. In such a case, a new lighting system design would need to be conducted again, resulting in both time and economic losses.

7. Conclusions

In any lighting system design, it is essential to consider the characteristics and influences that affect changes in the quantifiable parameters of lighting systems. The most significant among these influences are those that lead to a reduction in illuminance. Therefore, it is crucial to correctly identify these influences and quantify them during the design process to prevent a rapid decrease in the lighting system’s illuminance levels. The maintenance factor encompasses several of these characteristics, and its components can now be determined using software solutions. This article highlights the potential of using software support for determining the maintenance factor and the methods for calculating this parameter. The measurements taken in the production hall revealed insufficient illuminance levels. For a potential redesign, virtual models with four lighting system alternatives were created in the simulation tools Dialux and Relux, determining two methods for establishing the maintenance factor. Based on in-situ measurements and simulations, it was identified that the illuminance in the production hall was insufficient (below 300 lx). Following the Ergonomic Rationalization Sequence, a model was created in two simulation tools, with the sequence subsequently extended in the “Design of Lighting System” phase to include the determination of the maintenance factor as a necessary parameter for sustainability. Based on the achieved values and the percentage deviation in illuminance, it can be predicted that accurately calculating the maintenance factor, rather than using a fixed parameter, allows the system to use energy more efficiently over time. This approach aligns with sustainability goals by reducing energy waste and the associated carbon footprint. Additionally, a properly calculated maintenance factor (not automatically determined) can help minimize costs throughout the lighting system’s lifetime, as it predicts the need for lamp maintenance with minimal interventions, thereby maintaining the system’s optimal performance. The resulting satisfactory alternatives, which showed a 5% deviation between the methods used, underscore the importance of correctly determining the maintenance factor to ensure the energy, time, and cost efficiency of the lighting system redesign. Incorrect determination of the maintenance factor can lead to under- or over-sizing of illuminance. Under-sizing the lighting level can negatively impact visibility, workplace safety, and productivity. Conversely, oversizing the lighting due to an incorrect maintenance factor results in excessive energy consumption. If the maintenance factor is set too low, the designer assumes that the lighting system will deteriorate less than it actually will, affecting maintenance intervals, system lifespan, and operating costs. On the other hand, if the maintenance factor is set too high, the designer assumes that greater deterioration will occur, leading to unnecessary waste. In conclusion, when designing a lighting system using software tools, it is crucial to determine the dimensions of the system based on a maintenance factor calculated specifically for the lighting type. As confirmed by the results of this study, this has a significant impact on the illuminance, costs, and overall sustainability.