RAM MEASURE OF HYDRAULIC POWERED ROOF SUPPORTS IN UNDERGROUND LONGWALL COAL MINES

1. Introduction

Coal is a vital energy source and the first fossil fuel to be widely used. In many countries, conventional resources such as coal continue to play a significant role in energy production. Consequently, coal mines remain highly important, and countries possessing these resources generate substantial profits from their sale. Currently, most of the world's coal is extracted using the longwall mining method. This method employs various machines and equipment, including shearers, armored face conveyors (AFCs), conveyor belts, and power supports (Bołoz et al., 2022). Intense competition in the energy raw materials market drives mining companies to reduce production costs. In this context, it is anticipated that increasing production efficiency will help mitigate negative environmental impacts. One approach to achieving this goal is to continuously enhance the efficiency of equipment and machinery (Uth et al., 2019; Szurgacz et al., 2022a).

The longwall method is an underground mining technique known for its high production capacity and excellent recovery rates in coal mining. This method has consistently been regarded as the most cost-effective approach for coal extraction (Kazanin et al., 2022).

The most important machines used in this method include the drum shearer, AFCs, powered roof supports, and conveyor belt. Figure 1 shows a view of a longwall mine. In this method, mining operations are conducted beneath the powered supports. The shearer is mounted on the AFC and cuts the coal, dumping it onto the conveyor belt to be transported out of the mine. After the shearer has travelled the entire length of the face, it reverses direction and begins the next cut. As the shearer moves past the powered supports, the supports advance forward, closer to the newly cut surface. The powered supports protect workers and equipment along the working face, while the roof behind the supports is allowed to collapse as the face advances. Mining continues in this manner until the entire coal seam is extracted (Peng, 2019).

The primary function of power holders is to protect the mining operation from the adverse effects of the rock mass and its movement, while simultaneously advancing the shearer and AFC. Considering that a large number of units are required to support the roof, the capital invested in powered supports typically accounts for 60–80% or more of the initial investment in a longwall face, depending on the panel width (Peng, 2019). Furthermore, since powered supports in longwall mechanized mines are responsible for ensuring the safety of both equipment and workers, they are among the most critical components of mechanized mining operations and require proper monitoring and control of their operating parameters (Szurgacz et al., 2022b).

Figure 1. Scheme of the mechanized longwall mining operation (Guan et al., 2023)

Any failure or stoppage in powered support systems not only compromises the safety of the mining environment but also leads to the suspension of mining operations and, ultimately, the cessation of mine production. As mentioned earlier, from both economic and technical perspectives, powered supports are crucial pieces of equipment in longwall mining. Therefore, it is essential to study the failure behaviour and analyze the reliability and maintainability of this type of equipment. Focusing on the evaluation of RAM (Reliability, Availability, and Maintainability) in mining operations will enhance production, safety, and maintenance.

Mining equipment and systems comprise multiple interconnected components, and their effective operation is crucial for ensuring uninterrupted production within the mine. Due to the complexity of this equipment, it is essential to conduct research on its RAM. The RAM of mining systems and equipment has been a major emphasis in recent years. To date, considerable research has been conducted on the reliability of a variety of systems in the field of mining engineering. Table 1 provides a summary of the research articles related to reliability analysis of mining machinery and equipment utilized in underground mining opertion.

Table 1. A summary of studies on the reliability of underground mining equipment and machinery

Nowadays, reliability and maintainability of mining equipment and systems in underground coal mines such as shearer (Zhou et al., 2017; Ding et al., 2019; Wang et al., 2025), roadheader (Kahraman et al., 2023; Xiaofeng, 2025), discharge loaders (Sarkhel and 2015), and conveyors (Kumar 2016; Gorai et al., 2017; Masir et al., 2021) have been analyzed and discussed. Furthermore, numerous attempts have been made to analyze and optimize the load capacity of powered roof supports in longwall mining (Szurgacz et al., 2022; Szurgacz, 2024; Wang et al., 2023; Borska and Szurgacz, 2024). However, limited research has been conducted to study the reliability and maintainability of this critical system. Morshedlou et al. (2014) studied the reliability of powered supports at the Tabas coal mine using a statistical analysis approach. The results of this study indicate that the powered supports follow a Gamma reliability function. The reliability of the equipment decreases to nearly zero after approximately 520 hours of operation. Furthermore, the failure rate analysis reveals an improving trend, characterized by a decreasing failure rate. Stebnev et al., (2017) studied the downtimes of mining machines and equipment in the powered longwall mining systems at SUEK-Kuzbass Company, Russia. This paper analyzes and discusses the output per face, productivity, lifetime, and failure-free operation of mining machines, including shearers, powered roof supports, face conveyors, and electrical equipment. In the study, powered support systems produced by different companies were compared in terms of price, service life, and the unit cost of coal production. Failure analysis of equipment and systems over a seven-year period showed that the overall downtime due to mining machine failures was approximately 12%, with failures of powered support systems accounting for 13% of the total downtime. Szurgacz (2021) analyzed the effectiveness of powered roof supports. In this study, the overall equipment effectiveness (OEE) indicator was used to assess the degree of utilization. To achieve this, the availability, performance, and quality of powered roof supports were measured under conditions of tremor hazards and no tremor hazards. The results showed that the OEE of powered roof supports in mining tremor hazard conditions ranged from 55% to 65%, while in conditions without mining tremor hazards, it was estimated to be between 75% and 85%. The study noted that these OEE values indicate low effectiveness in utilizing roof support systems, highlighting the importance of reviewing operation and maintenance policies to improve this indicator.

A review of the aforementioned paper reveals that the reliability of powered support systems has been studied in only a limited number of research efforts. In Morshedlou et al. (2014), powered support systems are considered as a single system. However, breaking down an engineering system into its functional subsystems not only helps identify the subsystems that contribute most to system failure but also provides better insights into the failure behaviour of each subsystem. Moreover, this approach supports effective preventive maintenance activities without redundant planning. In Szurgacz’s (2021) study, the availability, performance, and quality of roof powered supports were addressed by considering only the mean time between failures, mean time to repair, total run time, and ideal cycle time. However, the failure and repair behaviour of the system were neither studied nor discussed. This paper aims to utilize reliability, maintainability, and availability indicators to predict the comprehensive performance of powered support systems, thereby ensuring the safety and continuity of the mining process. These indicators help improve the productivity of mining operations over their life cycle by reducing waste, maximizing profit, and ultimately optimizing overall life cycle costs. The results of this study provide valuable guidance and information to support decision-making related to the design and operation of powered support systems throughout their lifespan.

The paper is organized as follows. Section 2 describes the research methodology, presenting the statistical reliability and maintainability modelling approach. Section 3 details the hydraulic powered supports used in longwall mining operations. In Section 4, the reliability, maintainability, and availability of the powered support system at Parvadeh Tabas Coal Mine, Iran, are analyzed and discussed.

2. Methods

Powered supports emerged following the development and advancement of steel support systems, such as friction and hydraulic supports, used in longwall mining operations. The powered roof support system consists of several hydraulic supports. A solid canopy is positioned on top of these supports, while their bases are connected to AFC components. As the mining face advances, the supports retract and move forward, controlled remotely. Figure 2 shows a view of a powered support.

The powered support system can be divided into two main subsystems: mechanical and hydraulic. The mechanical subsystem includes the following main components:

- The canopy transfers the pressure from the rock mass to the support.

- The shield support consists of a gob shield and side shields.

- Lemniscate tie rods connect the gob shield to the base in a straight-line system, providing stable support and maintaining an almost constant distance between the canopy end and the face front throughout the entire range of roof support heights.

- Legs consists of a double-acting hydraulic ram and a telescopic relay bar.

- Floor base to transmit the bearing force of the support to the floor.

- Shifting system consists of a ram installed in the body that acts directly on the advancing system's bar, causing the front of the base to lift during support advancement. This system aids in advancing the support.

The hydraulic system operates various rams, including a two-stage telescopic hydraulic leg, tilt ram, correcting ram, support advancing ram, base lifting ram, base correction ram, supporting ram, ram for maintaining the last support on an incline, face sprag ram, and additional devices for operating the support system on inclines.

Figure 2. Arrangement of a powered roof support (1: canopy, 2: shield support, 3: lemniscate tie rods, 4: leg, 5: floor base, and 6: shifting system) (Szurgacz et al., 2022b).

In modern longwall mining, self-advancing, hydraulically powered supports are used to extract coal at the face. These powered supports hold up the roof, ensuring a safe working environment for miners, face equipment, and other mining operations. Additionally, the hydraulically powered supports push the AFC and advance themselves.

There are four basic designs of powered supports: frame, chock, shield and chock shield. The frame support can be considered an extension of the single hydraulic props that are often used underground and it is the first type of powered support designed for modern self advancing hydraulic powered supports. The basic arrangement has two hydraulic props or legs in an vertically arranged series connected at the top with a one or two part canopy, which is frequently referred to as just a canopy (Peng, 2019). As opposed to the frame support, chock supports have a solid canopy while the base is composed of two separate pieces that are joined by steel bars, joined at the front or back. This difference is substantial.

Shields are defined as the posterior extension of the caving shield-lemniscate-link system between the base and the canopy. The caving shield is typically pitched and hinged-jointed to the canopy; and the connection with the base is through either the lemniscate assembly or direct. This essentially renders the shield a cinematically stable support, an advantage to frame and chocks. In shields the hydraulic legs are pitched to allow greater range of working height and for more room for traffic. Chock shields have the main advantages of both the chock and shield. Essentially the chock shield is a shield with four legs between the canopy and the base plates. The legs can be oriented vertically or in a V-configurations between the canopy and base, and this is the rationale for the name "chock shield." Chock shields provide the most efficient support and particularly advantageous in hard roof conditions (Peng, 2019).

The performance evaluation of powered support systems in underground coal mines is conducted using various criteria. To date, multiple tools have been employed to analyze operational and maintenance performance in equipment and machinery production management. The effectiveness of powered support systems is assessed based on their reliability and ease of maintenance.

Reliability, which can be adequately assessed using failure analysis, is used as a proxy for performance (Cai et al., 2021). Reliability (R) is defined as the probability that equipment or processes will perform reliably as designed (and identified) without "failure" when used exactly as intended over a specified time period under stated conditions. The formal mathematical definition of reliability is given by Equation 1:

[TeX:] R(t) = 1 - F(t) = 1 - \int_{0}^{t}{f(x)dx}$R\left(t\right)=1-F\left(t\right)=1-{\int }_0^{t}f\left(x\right)dx$ (1)

Where:

R(t) is the reliability at time t,

F(t) is the cumulative failure distribution function,

f(x) is the failure probability density function.

Complex systems contain numerous mechanical, hydraulic, or electrical subsystems. Each subsystem can fail, which increases the probability of overall system failure. The resulting reliability depends on the reliability of the individual subsystems and their configuration. Series and parallel are two fundamental system arrangements. A series system generally consists of a group of independent subsystems in which the failure of any one component causes the entire system to fail. In contrast, a parallel system fails only if all its subsystems fail. The reliability of series and parallel systems is calculated as Equations 2 and 3:

[TeX:] R_{s} = \prod_{}^{}R_{i}$R_s={\prod }_{}^{}{R}_i$ (2)

[TeX:] R_{P} = 1 - \prod_{}^{}{(1 - R_{i})}$R_P=1-{\prod }_{}^{}(1-R_i)$ (3)

Where:

[TeX:] R_{s}\ $R_s$and [TeX:] R_{P}\ $R_P$are the reliability of the whole system in series and parallel configurations, respectively,

[TeX:] R_{i}$R_i$ denotes the reliability of the ith independent subsystem.

Maintainability refers to the capability of an item to be kept in, or restored to, a designated state when maintenance is conducted. The Equation 4 defines the maintainability function.

[TeX:] M(t) = 1 - F_{r}(t) = 1 - \int_{0}^{t}{f_{r}(x)dx}$M\left(t\right)=1-F_r\left(t\right)=1-{\int }_0^t{f}_r\left(x\right)dx$ (4)

Where:

[TeX:] M$M$ (t) is the maintainability at time t,

[TeX:] F_{r}(t)$F_r\left(t\right)$ is the cumulative repair distribution function,

[TeX:] f_{r}(x)$f_r\left(x\right)$ is the probability density function of the repair time.

If a system contains independent subsystems, the maintainability of a series (M_S) and parallel (M_P) system can be determined as Equations 5 and 6:

[TeX:] M_{s} = \prod_{}^{}M_{i}$M_s={\prod }_{}^{}{M}_i$ (5)

[TeX:] M_{P} = 1 - \prod_{}^{}{(1 - M_{i})}$M_P=1-{\prod }_{}^{}(1-M_i)$ (6)

Where:

[TeX:] M_{i}$M_i$ is the maintainability of ith subsystem.

Availability is defined as the probability that the equipment or a system can perform its intended function at time t, whereby the time period includes the running time, logistical time, active repair time, and administrative time. Calculating availability (Av) requires obtaining the uptime and downtime, as shown in Equation 7 (dos Santos Silva et al., 2016).

[TeX:] Av = \frac{MTBF}{MTBF + MTTR}$Av=\frac{MTBF}{MTBF+MTTR}$ (7)

The MTBF represents the average failure time and the MTTR denotes average repair time for the machine. The inverse of availability corresponds to unavailability, or the expected downtime ratio, which is crucial for assessing operational risk.

Considering the structure and network of the system’s availability, the overall availability of a system with i serial or parallel subsystems (Av_i) can be calculated as follows:

[TeX:] {Av}_{s} = \prod_{}^{}{Av}_{i}${Av}_s={\prod }_{}^{}{Av}_i$ (8)

[TeX:] {Av}_{P} = 1 - \prod_{}^{}{(1 - {Av}_{i})}${Av}_P=1-{\prod }_{}^{}(1-{Av}_i)$ (9)

Where:

[TeX:] {Av}_{s}${Av}_s$ and [TeX:] {Av}_{P}${Av}_P$ represent the availability of the whole system in serial and parallel configurations, respectively.

Since failures cannot be entirely prevented, periodic inspection and measurement can help mitigate the severity of any failure. To ensure a system functions properly, regular maintenance activities are essential. Maintenance tasks can be divided into two main categories: planned and unplanned. Preventive maintenance (PM) consists of preplanned actions aimed at reducing the impact of known causes that could potentially lead to system breakdowns (Duffuaa and Raouf, 2015, Rahimdel and Ghodrati 2022). PM activities aim to increase the productivity of equipment and facilities while reducing maintenance and insurance expenses (e Costa et al., 2012). In fact, such maintenance operations are performed to ensure the system continues to operate satisfactorily by conducting systematic inspections that identify and address potential failures before they grow into critical issues (Wang and Pham, 2006). PM is planned and performed after a specified period or following a certain amount of system usage to reduce the likelihood of failure (Basri et al., 2017).

The remainder of this section is devoted to presenting the methodology for estimating reliability and maintainability indicators. Data collection, including failure and repair times, is the first step. After establishing the database, an appropriate method is selected for the statistical modelling of the system’s performance indicators.

The step-by-step procedure for reliability modelling of a repairable system is illustrated in Figure 3. This detailed flowchart facilitates dataset modelling and serves as a foundation for analyzing failure and repair data.

These processes are commonly known as stochastic point processes (Andrzejczak, 2015). For the reliability assessment of repairable systems, three different methods are typically employed:

- Renewal Process (RP)

- Homogeneous Poisson Process (HPP)

- Nonhomogeneous Poisson Process (NHPP)

In repairable systems, the condition of a system after maintenance actions can vary depending on the effectiveness of the repair. In some cases, the system may be restored to a state equivalent to its initial condition, while in others it may retain characteristics of prior deterioration or fall somewhere between these two extremes. When repairs fully restore the system to its original performance level, the failure–repair sequence can be described using a renewal framework. Within this framework, system failures followed by repairs are treated as recurrent events with statistically independent and identically distributed intervals. Analytical expressions for the renewal function are available only for a limited class of probability distributions, including exponential, gamma, and phase-type models (Arnold et al., 2020; Muralidharan, 2008).

The Homogeneous Poisson Process (HPP) is a special type of renewal process in which the inter-event times are independent and identically distributed (iid) exponential random variables. The HPP is an orderly stochastic point process characterized by stationary and independent increments. In an HPP, the inter-arrival times are independent and exponentially distributed with parameter λ, and the number of events follows a Poisson distribution. In reliability studies, the HPP has been used to model failures in repairable systems during certain phases of the system's life. However, because the arrival rate in an HPP is constant, it is inadequate for modelling system wear-out or growth, or for developing policies related to maintenance, overhaul, or trade-in (Muralidharan, 2008; Arnold et al., 2020).

For systems in which maintenance actions do not restore the system to a like-new condition, the non-homogeneous Poisson process provides a more realistic modelling approach. This framework relaxes the assumption of complete renewal by allowing the failure occurrence rate to vary as a function of time, thereby reflecting minimal or imperfect repair scenarios. As a result, NHPP models are particularly effective in representing systems whose failure behaviour evolves due to aging, operational stress, or maintenance interventions. The inclusion of a time-dependent intensity function enables the analysis of changing failure patterns throughout the system’s operational life, especially in environments characterized by periodic inspection and maintenance activities (Saldanha et al., 2001; Asfaw and Lindqvist, 2015; Slimacek and Lindqvist, 2016; Brown et al., 2023; Yarmohammadi et al., 2022).

Figure 3. Process flowchart for the reliability and maintainability modelling

The selection of each modelling methodology is based on the type of data being collected. As mentioned in Figure 3, a testing procedure is conducted to perform trend and serial correlation tests on the data to assess the attribute and procedure to model the data. If a trend is detected in the failure data, an NHPP approach (e.g. a power-law process) is employed to model the data. If a trend is not present, the data will be analyzed for the testing of the presence or absence of serial correlation. If serial correlation is evidenced, an HPP approach (e.g. a branching Poisson process) is assumed to model the data. If there is neither a trend nor serial correlation, the failure data are considered independent and identically distributed (iid), and the appropriate statistical model is fitted to the data. A Renewal Process (RP) and classical statistical methods treat the data as a counting process where the inter-occurrence times are iid with an arbitrary life distribution.

Goodness of Fit (GoF) measures the extent to which a statistical model fits a set of observed data. GoF indices quantitatively summarize the differences between the observed data values and those predicted by the model (Maydeu-Olivares and Garcı´a-Forero, 2010). In this test, the decision to reject or fail to reject the null hypothesis for each candidate distribution is made by comparing the test statistic to its critical value at a specified significance level (Joo et al., 2018). There are different GoF tests such as Anderson-Darling (Scholz and Stephens, 1987), Cramer-von Mises (Darling, 1957), chi-square (Albrecht, 1980), and Kolmogorov-Smirnov (Massey, 1951). In this paper, the Kolmogorov–Smirnov (K–S) test was employed. This test compares the empirical cumulative distribution function of the data with the theoretical cumulative distribution function of various candidate models. If the p-value of the K–S test exceeds the significance level of 0.05 (or, equivalently, if the test statistic is less than the critical value), the null hypothesis is not rejected, and the distribution is considered an acceptable model (Joo et al., 2018).

This section continues with methods for assessing trends and correlations in failure data. The failure pattern of a device is referred to as a trend. Various methods exist to examine the presence or absence of trends in failure data. In graphical methods, if the time interval between system failures increases, the system exhibits a decreasing trend. Conversely, if the time interval between failures decreases as the device ages, the system shows an increasing trend. If there are no significant changes in the time between failures throughout the device's life, the device is considered stable and trend-free. Several graphical and analytical tests are available to assess trends in data. In graphical tests, the cumulative failure time is plotted against the cumulative number of failures. A straight line indicates no trend, while convex or concave curves suggest the presence of a trend. However, graphical methods have limited reliability. The Military Handbook-189 (1986) analytical method is one of the most widely used techniques for assessing trends in failure data. In this method, the U index is calculated using the Equation 10 to evaluate the trend (Ascher and Feingold, 1984; Kumar and Kelefsjo, 1992; Saldanha et al., 2001):

[TeX:] U = 2\sum_{i = 1}^{n - 1}{ln(T_{n}}/T_{i})$U=2{\sum }_{i=1}^{n-1}ln(T_n/T_i)$ (10)

Where:

n is the total number of failures,

Tₙ is the time of the nth failure,

Tᵢ is the time of the ith failure.

Assuming the null hypothesis is a HPP, U has a Chi-squared (Chi²) distribution with 2(n-1) degrees of freedom. Serial correlation can be visually determined by plotting the ith TBF against the (i-1)th TBF. If the plotted values present as a random scatter with no apparent trend, then the TBFs are free from serial correlation.

4. Results

This part of the study is dedicated to measuring the reliability, maintainability, and availability of powered support systems in Tabas Parvadeh Coal Mine. Parvadeh No. 1 Mine in Tabas has been built as the first mechanized coal mine in Iran, with the annual production capacity value between 1.2 to 1.5 million ton of coal. The Parvadeh coal area is located in an extent of approximately 12,000 square kilometres and consists of a geological reserve of 1.1 billion tons of coking coal. It is one of the four coal regions in Tabas, where it is the largest coking coal deposit in Iran. The location of the area lies 75 km south of Tabas city in the South Khorasan Province. The total number of coal seams in Parvadeh No. 1 is 15 coal seams. Within a 100-meter stratigraphic interval, five main seams of coal designations are identified as D, C1, C2, B1, and B2. Figure 4 shows the stratigraphic column of Parvadeh coal basin in Tabas.

Among the coal seams in this area, only seam C1 is currently being mined. The mining operation utilizes the longwall backward method. This study evaluates the powered support systems used in the W3 panel. The specifications of this mining workshop are presented in Table 2. Figure 5 illustrates the layout of the panels in the mine under study.

Table 2. The specifications of the panel W3 (TPCC, 2024)

This paper aims to conduct a RAM analysis of the hydraulic FAZOS-10/28 POz powered roof supports (manufactured by FAZOS Company) used in the Parvadeh Tabas Coal Mine. In this context, the P means chock support, O refers to shield support, and z refers to roof support designed to operate with the roof caving system. This support system features a two-leg design, equipped with two telescopic hydraulic legs. The subassemblies of the support system are connected to each other through joint components and powered hydraulic elements.

Figure 4. The coal basin stratigraphic column of Tabas Coal Mine (Molayemat and Mohammad Torab, 2017; Jozanikohan et al., 2021)

Figure 5. Layout of Various Panels in the Parvadeh-I Coal Mine (Aghababaei et al. 2019)

A database of support system downtimes, collected over an 18-month period, was divided into two subsystems that impeded the machine's ability to operate and progress. The parameters were time between failures (TBF), time to repair (TTR), cumulative TBF and TTR, and total hours of breakdown time for each system. The dataset for RAM Analysis was from daily reports over an 18-month period of mining operations. The data tables were designed to sort and arrays the data chronologically. The TBFs and TTRs when stoppages and repairs were incurred within each subsystem were categorized for reliability and maintainability (RAM) analysis.

After gathering and preparing the failure and repair data, trend and serial correlation tests were carried out with the intent to select suitable reliability and maintainability modelling methods. In consideration of the tests considered, the accompanying computed values of both the test statistic (Equation 10) and the Chi² test statistic were used. Table 3 provides the results. According to Table 2, there was no trend in the TBF and TTR data of the mechanical subsystem and the TTR data of the hydraulic subsystem; furthermore, there was no correlation. Thus, these datasets were iid, and the renewal process (RP) method was decided on, for modelling reliability. The TBF data of the hydraulic subsystem did show an increasing trend; thus, the NHPP modelling method, based on the power-law process (PLP), was used for reliability modelling. EasyFit software (Schittkowski, 2002) was used to apply the Kolmogorov–Smirnov statistical test to identify the best-fitting distributions and their parameters. The results of the analysis of data and stated parameters are shown in Table 4. Using Table 3 and Equation 1, the reliability functions were calculated. The resulting reliability plots for subsystems are presented in Figure 6. The reliability of the powered support system as a whole is calculated using Equation 2 and is shown in Figure 7.

Using the calculated parameters for the best-fitted distributions of the TTR data, as shown in Table 3, the maintainability of both subsystems was plotted and presented in Figure 8. Furthermore, based on the serial configuration of the subsystems, the maintainability of the entire system was calculated using Equation 5 and illustrated in Figure 9.

Table 3. The test statistic U at a 95% confidence level for TBF and TTR data

Table 4. The best-fit distribution for TBF and TTR data

Figure 6. Reliability plots for subsystems of powered support system

Figure 7. Reliability of the powered support system in Tabas Coal Mine

Figure 8. Maintainability plots for subsystems of powered support system

Figure 9. Maintainability of the powered support system in Tabas Coal Mine

Availability is the probability that equipment or a system is functioning satisfactorily under specified conditions. Conversely, it represents the likelihood that the system is neither failed nor undergoing repair when it is needed. To calculate the availability index, the mean time between failures and the mean time to repair are first determined. The availability index is then calculated using Equation 7. The results are presented in Table 5.

Table 5. Availability of powered roof support’s subsystems

5. Discussions

Regarding the reliability analysis, the reliability of both subsystems decreased to 85% after 25 hours. After 25 hours, the reliability of the hydraulic subsystem remains higher than that of the mechanical subsystem throughout all time intervals. This indicates that the service life of the hydraulic subsystem is longer compared to the mechanical subsystem. For instance, the reliability of the mechanical and hydraulic subsystems drops to 50% at 85 hours and 155 hours, respectively. It should be noted that the reliability of both subsystems approaches zero at approximately 1100 hours. Due to the series configuration of the subsystems, failure in any one subsystem halts the overall operation. On the other hand, the powered support system functions only when both subsystems are functioning properly. As seen in Figure 6, the failure probability of the powered support system increases to 20% after only 19 hours of operation. The reliability of the entire system decreases to 50% within 52 hours. This downward trend continues until 300 hours, at which point the system reliability falls below 4%.

Referring to maintainability analysis, the maintainability at the beginning of the repair operation steadily increased. The maintainability of the mechanical and hydraulic subsystems reached 90% and 95%, respectively, after one hour of repair. This indicates that most failures in these subsystems are repaired within one hour. The maintainability plot of the whole system (see Figure 7) shows that 85% of failures require one hour to be repaired. The maintainability of the powered support systems exceeds 95% at 2.5 hours. Conversely, only 5% of failures require more than 2.5 hours to be repaired and return to their active state.

High availability for both subsystems is reported in Table 5. This indicates that both subsystems can operate consecutively at a high level of functioning without interference. Since the availability structure is a series configuration, the availability of the whole system can satisfy Equation 8 as follows:

The availability analysis of powered roof supports indicates the probability of the powered support systems in Tabas Coal Mine operating under desirable conditions is 99.58%.

The results of the reliability and maintainability study indicate that the powered support system in the Parvadeh Mine operates with a significant probability of failure; however, these failures can be repaired promptly. This suggests that the preplanned tasks are designed to address the known causes of potential failures and reduce the frequency of premature failures. The preventive maintenance (PM) can be either condition-based or based on the historical data of equipment failures. In this study, the PM intervals were estimated using a reliability model. To achieve this, an 80% reliability threshold was considered to ensure both efficiency and performance. On the other hand, the probability that the equipment will continue operating without failure from the start of operation until the next PM action is at least 80%. Equivalently, this means that a maximum failure probability of 20% is accepted before performing preventive maintenance. Regarding the reliability analysis, the reliability of the powered support system in the Parvadeh mine decreased to 80% after approximately 18 hours. This means that if no maintenance is performed before the start of the fourth shift, the failure probability of the powered support system increases to 20%. In this approach the reliability-based maintenance intervals for mechanical and hydraulic subsystems is calculated as about 19 hours. Given that inspecting power retainers during coal mining operations might interrupt the continuity of the mining process, it is recommended that these inspections be conducted at the end of each work period, approximately every 22 hours. Following this schedule, the powered support system will achieve a reliability of approximately 76%. After preventive maintenance, the equipment will operate at full (100%) reliability at the start of the next working day.

6. Conclusions

This research assessed the reliability, maintainability, and availability of powered support systems with a focus on the Parvadeh Tabas Coal Mine in Iran. Initially, all main components and items of the support systems were classified into two serial subsystems: mechanical and hydraulic. Afterward, failure and repair data for each subsystem were collected and analyzed through an 18-month study. Results show that the reliability of both subsystems drops to zero around 1100 hours. Maintainability analysis showed that most failures of the support system are repaired within one hour. Availability analysis revealed that the overall system availability exceeds 99 percent. To establish an effective and manageable maintenance schedule for powered support systems, it was recommended that the preventive maintenance tasks be performed at the end of each working day.

The findings of this study help identify the causes and problems occurring during the operation of roof supports. They provide a basis for implementing improvements and corrective actions to enhance the effectiveness of the coal production process in mechanized longwall mines. Nevertheless, this study has some limitations that can be addressed in future research. The failure behaviour of additional subsystems, such as control systems and sensors—both critical to machine performance—can be incorporated into RAM analysis. Moreover, studying the economic dimensions through the analysis of maintenance costs and coal production losses due to powered systems’ failures is proposed for future research. Additionally, environmental factors such as dust, humidity, temperature, and terrain conditions that may affect support systems should also be considered in future studies.

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Funding

This research was funded by University of Birjand, Birjand, Iran

Author’s contribution

The author prepared the whole work.