Reliability maintenance aspects of deck machinery for ageing/aged fishing vessels

Abstract

This paper addresses the reliability aspects regarding deck machinery on ageing/aged fishing vessels. A reliability and degradation analysis for ageing fishing vessel deck machinery enables an evaluation of the reliability trend for the remaining useful life of the deck machinery. The research was limited to the maintenance of cranes, winches and capstans and failure data for fishing vessels whose age was 24 years and above. The reliability and degradation of deck machinery for ageing fishing vessels was analysed using Weibull and Gamma distributions. Deck machinery maintenance and failure data collected from fishing vessels at Walvis Bay were statistically fitted using ReliaSoft from which an analysis of deck machinery reliability was done. Reliability and degradation results of deck machinery indicate that the majority of the fishing vessels are using deck machinery beyond their design life. For the two mission end times 4700 h and 23300 h, winches and cranes have lower reliability results, less than 50% compared to capstans. This is attributed to the age of the fishing vessels and maintenance laxity towards deck machinery which poses risks to both operators and the vessel crew. The maintenance of ageing fishing vessel deck machinery should be given priority in order to extend their time available for the better fishing operations.

Nomenclature

CMMS	=	Computerised Maintenance Management System
FM	=	Fisher’s Matrix
IEEE	=	Institute of Electrical and Electronics Engineers
MED	=	Median
MIL HDBK	=	Military Handbook
MFMR	=	Ministry of Fisheries and Marine Resources
MTBF	=	Mean Time Before Failure
MTTF	=	Mean Time To Failure
NEEZ	=	Namibia Exclusive Economic Zone
NLRR	=	Nonlinear Rank Regression
NSWC-11	=	Naval Surface Warfare Center (2011 Edition)
OREDA	=	Onshore/Offshore Reliability Data
PME	=	Plant Machinery & Equipment
RUL	=	Remaining Useful Life

1. Introduction

Incident and accident investigation reports by the International Maritime Organisation (IMO) have shown that approximately a quarter of all maritime incidents and accidents are due to the machinery failure (Dobie 2015; Islam et al. 2017). The International Labour Organisation (ILO) and Food and Agriculture Organisation (FAO) estimate that 7% of all fatalities at work occur in the fishing industry, which is attributed to machinery age, poorly maintained machinery, human error and incorrect operation of machinery (Wang et al. 2005; Titulo 2011; Handani and Uchida 2014).

The lifetime of deck machinery may be described in the form of a bathtub curve that consists of three distinct phases, as indicated in Figure 1. In the first phase, the initial failures can be attributed to the design and manufacturing faults once the machinery is put to use. The second phase, which is the most useful lifetime of the deck machinery, has the least failures as a result of the proper maintenance and appropriate operation of the machinery. The last phase is the wear-out where ageing and degradation conditions may lead to multiple failures (BAE Systems Ltd 2002). An aged vessel is the one that may be considered to have lived its useful lifetime. The vessel’s life expectancy ranges between 23 and 25 years (Loughran et al. 2002). Ageing in this context refers to the gradual degradation of the internal processes in the systems that bring the systems closer to failure (Nima et al. 2009).

Figure 1. Bathtub curve.

The remainder of this paper is organised as follows: Section 2 presents deck machinery and their use on a vessel, deck machinery maintenance approaches and system ageing relationship with reliability. Section 3 discusses reliability models taking into consideration the Weibull and degradation models. A detailed description, of the case study, problem, presentation and discussion of the results, is presented in Section 4. In Section 5, conclusions and observations have been summarised with directions for future work identified.

2. Deck machinery and maintenance approaches towards ageing fishing vessels PME systems

2.1. Deck machinery

Deck machinery plays a critical role in the stability of vessels during towing, docking, lifting, anchoring loading and offloading. Failure of deck machinery during operation can lead to undesired catastrophes. Deck machinery failure is prominent in aged and ageing fishing vessels where there is infrequent maintenance attention compared to other Plant, Machinery and Equipment (PME) systems. A typical fishing vessel is fitted with winches (hydraulic, trawl), capstans (drums, stem) and hydraulic crane(s), all of which are considered in this paper as indicated in Figure 2.

Figure 2. (a), (b) Deck machinery.

2.2. Maintenance approaches

The maintenance of deck machinery on fishing vessels depends on the operators/owners’ policies. Inspection and minor maintenance of deck machinery while at sea is carried out by the maintenance crew, this accounts for less than 10% of annual maintenance works on deck machinery. Depending on the age of the fishing vessel, owners/operators tend to optimise on revenue returns rather than maintenance strategies. Major maintenance works are done at the shipyards/docks. The duration between dock discharge and next maintenance for deck machinery is estimated to be as long as eight months to two years. This poses reliability concerns for such machinery, as they are susceptible to break down without prior warning.

The current advancement in marine vehicle compact designs has resulted in low maintenance space when executing maintenance activities onboard (Arab et al. 2013). In the maritime industry, marine vehicle maintenance is generally grouped into three categories: corrective, preventive and predictive maintenance (Lazakis et al. 2009). Corrective maintenance: the plant, machinery or equipment must first breakdown before it is restored back to its operation mode. Scheduled/Preventive maintenance: the repair and servicing of physical assets (equipment/facilities) with the aim of keeping them in an acceptable operational state through methodical inspection, detection, and correction of possible emerging failures before happening and possibly developing into major failure (Rausand and Vatn 1998; Emovon 2016). Condition-based maintenance: the maintenance practice that tolerates and detects/failures of a system by continuous monitoring of the system during operation (Adriaan and Rob 2015).

2.3. Degradation

Degradation can be regarded as the general reduction in the reliability, performance and useful life span of a given asset (Nima et al. 2009; Shahrakia et al. 2017). The use of degradation data in the assessment of the reliability of mechanical systems has become one of the approaches to determine the safety and operational capacity of ageing mechanical systems. Degradation data can be used to predict the useful remaining life of deck machinery for fishing vessels and establish their current degradation status. Fishing vessel deck machinery degrade as they age due to environmental conditions, usage and maintenance approaches, thus resulting in the reduction in their reliability. Once a certain degradation threshold is reached, the deck machinery will operate on a higher probability of failure. Like in the maritime industry, the determination of the degradation threshold values for deck machinery is not specified rather on the dependence on historical performance of the deck machinery (Singpurwalla 2006; Pan and Balakrishnan 2011).

3. Reliability lifetime via Weibull distribution

The reliability and degradation of marine mechanical PME systems can be analysed by different methods. Weibull distribution method is one of the widely accepted methods for estimating the reliability of marine mechanical PME systems. In this paper, the reliability and degradation of deck machinery for ageing fishing vessels is analysed by Weibull and Gamma distribution methods, respectively. Weibull probability distribution function is used to estimate the parameters related to failure in Equation (1) and reliability equation (3) of mechanical PME systems of fishing vessels. The assumption put forward is that all the fishing vessels conform to the IMO standards (1) $\begin{array}{rl}f(t)& =1-e^{-{[t/\alpha ]}^{\beta }}\end{array}$(1) (2) $\begin{array}{rl}f(t)+R(t)& =1\end{array}$(2)

From which the reliability of the system R(t) can be deduced as (3) $R(t)=1-f(t)=e^{-{[t/\alpha ]}^{\beta }}$(3) where t is the time until failure after the maintenance of a particular PME system, $\alpha$ is the scale parameter and $\beta$ is the shape factor of the system.

The values of $\alpha$ and $\beta$ are the Weibull’s parameters used to interpret the model results. The $\beta$ parameter indicates whether the failure rate of the mechanical PME system is increasing, decreasing or constant and the $\alpha$ parameter is a measure spread in the distribution data (Albernethy 2006)  The Weibull’s parameters $\alpha$ and $\beta$ (for each PME system) are determined from optimising the likelihood function L, in Equation (4). This results in Equations (6) and (7) with respect to the parameters $\alpha$ and $\beta$, respectively for numerical accuracy (Abernethy 2006) (4) $\begin{array}{rl}L(\alpha ,\beta ,t)& =\prod _{j=1}^{n}f(t)\end{array}$(4) (5) $\begin{array}{rl}\mathrm{\forall },f(t)& =f(\alpha ,\beta ,t)\end{array}$(5) (6) $\begin{array}{rl}\frac{\text{d}L(\alpha ,\beta ,t)}{\text{d}\alpha }& =\prod _{j=1}^n\frac{\text{d}f(t)}{\text{d}\alpha }=\prod _{j=1}^n\left[\frac{\text{d}}{\text{d}\alpha }\{1-e^{-{[t/\alpha ]}^{\beta }}\}\right]=0\end{array}$(6) (7) $\begin{array}{rl}\frac{\text{d}L(\alpha ,\beta ,t)}{\text{d}\beta }& =\prod _{j=1}^n\frac{\text{d}f(t)}{\text{d}\beta }=\prod _{j=1}^n\left[\frac{\text{d}}{\text{d}\beta }\{1-e^{-{[t/\alpha ]}^{\beta }}\}\right]=0\end{array}$(7)

Equations (6) and (7) result into Equations (8) and (9), respectively; (8) $\begin{array}{rl}& \frac{1}{\alpha }\left[n+\left\{\frac{1}{\alpha }\sum _{j=1}^n{t_j}^{\beta }\right\}\right]=0\end{array}$(8) (9) $\begin{array}{rl}& \frac{n}{\beta }\left[1-\sum _{j=1}^n{t_j}^{\beta }In(t_j)\right]+\sum _{j=1}^{n}In(t_j)=0\end{array}$(9) For the parameters $\alpha$ and $\beta$, Equations (8) and (9) are solved using numerical iteration and the parameters $\alpha$ and $\beta$ are obtained.

For a three-parameter Weibull distribution (10) $\begin{array}{rl}f(t)& =\frac{\beta }{\alpha }{\left(\frac{t-\mu }{\alpha }\right)}^{\beta -1}{e}^{-{[(t-\mu )/\alpha ]}^{\beta }}\end{array}$(10) $\begin{array}{rl}\mathrm{\forall },f(t)& =f(\alpha ,\beta ,\mu ,t)\ge 0,t\ge 0,\alpha >0,\beta >0\\ & \text{and}-\mathrm{\infty }<\mu <\mathrm{\infty }\end{array}$ $\begin{array}{rl}\mu & =\text{failure free time or location parameter}\end{array}$

3.1. Degradation models

Gamma degradation models are useful in describing the deterioration of systems as a result of accumulated wear and tear (Avramidis et al. 2004; Pan and Balakrishnan 2011; Shahrakia et al. 2017). Accumulated wear and tear for deck machinery result from non-conformity to the set maintenance schedule(s) and laxity towards replacing worn-out parts or components that are due for replacement. The accumulated wear and tear results in component mass loss, volume loss and skewed machinery dimensions (Blau 2017). Profilometry, direct dimension measurements and microscopy techniques are some of the methods being used to measure accumulated wear and tear (Basumatary and Wood 2017; Voss et al. 2017; Farfan-Cabrera et al. 2018; Huang et al. 2018). Length, depth, mass, time to wear, critical load, wear coefficients are some of the measures that can be used to quantify wear.

The gamma process of modelling results into an exact probability distribution function is thus used in deterioration modelling for maintenance (Nguyen et al. 2018). Degradation data can be measured by either physical-based approaches or data-driven approaches (Xiang et al. 2018). The physical-based approaches rely on mathematical formulation, while the data-driven approaches rely on machinery degradation data. The data-driven methods include a trend-based approach and an artificial intelligence approach (Sun et al. 2014; Zhou et al. 2014).

The gamma degradation process model that describes the ageing behaviour of a system is presented in the following;

If

• d_t is the degradation status of a particular deck machinery at a time t;

• f_T is the failure threshold of the deck machinery (provided the degradation exceeds f_T);

• t_fd is the first time of the deck machinery degradation process to level f_T.

Then, the first time lapse for the degradation process is given in Equation (11) as adopted from (Wang et al. 2018) (11) $t_{fd}(f_T)=inf\{t\in {\mathbb{R}}^{+},d_t\ge f_T\}$(11)

The time lapse before t_fd is the remaining useful lifetime (RUL) of the deck machinery that is denoted as $RU{L}_{(d_t,t)}$

Increments of degradation of deck machinery through a gamma process modelling (d_t),(d_t – d_s) are treated to be independent thus resulting in (12) $d_t-d_s\sim G([\alpha (t)-\alpha (s)],\beta )$(12) $\mathrm{\forall },\alpha (t)$ is the distribution shape function, $\beta$ is the scale parameter, $\alpha (t)$ is an increasing function that is either linear ($\alpha (t)=\gamma t$) or nonlinear ($\alpha (t)=\gamma t^{\epsilon }$), $\mathrm{\forall },0<\gamma \le \mathrm{\infty }$ and $\epsilon >1$

The gauss process transition probability for the deck machinery for two degradation states a, b is deduced in Equation (13): $\begin{array}{rl}& \mathrm{\forall },a<b\end{array}$ (13) $\begin{array}{rl}P(a,t\text{|}b,s)& =\frac{{\beta }^{{}^{\alpha (t)-\alpha (s)}}}{\mathrm{\Gamma }[\alpha (t)-\alpha (s)]}(a-b{)}^{\alpha (t)-\alpha (s)-1}{e}^{-(a-b)}\end{array}$(13) where $\mathrm{\Gamma }$ is Euler’s Gamma function. The mean and variance of the degradation level of the deck machinery are given by Equations (14) and (15), respectively (14) $\begin{array}{rl}E(d_t)& =\frac{\alpha (t)}{\beta }\end{array}$(14) (15) $\begin{array}{rl}V(d_t)& =\frac{\alpha (t)}{{\beta }^2}\end{array}$(15) The cumulative degradation distribution function in Equation (16), of the RUL at a degradation level $d_t=b$, at any time t of the deck machinery, is in line with (Paroissin and Salami 2014) model (16) $F_{RU{L}_{(b,t)}}(u)=\frac{\mathrm{\Gamma }(\alpha (u+t)-\alpha (t),(d_t-b_t)/\beta )}{\mathrm{\Gamma }(\alpha (u+t)-\alpha (t)}$(16)

4. Case study

During 2012/2013, a total number of 256 vessels were licensed to operate in the Namibian Exclusive Economic Zone (NEEZ) (MFMR 2014). There are only two ship maintenance yards separated by a distance close to 400 m at Walvis Bay, Erongo region. Not all the vessels that are licensed to operate in the NEEZ are maintained at the two shipyards but rather at other shipyards in South Africa, Angola and other western African countries. It is imperative to note that this paper only considered fishing vessels whose age was more than 25 years. This section demonstrates how failure and degradation data of ageing fishing vessels are useful in determining and predicting the reliability status of the deck machinery from which the maintenance strategies of such machinery beyond their useful life time are suggested.

4.1. Methods and materials

Deck machinery maintenance and failure data were sourced from maintenance records that were readily available on some vessels and from cross-interviews with the maintenance crew. The maintenance and failure data were collected from eight fishing vessels whose average age was 25 years from both the floating dock and synchrolift repair yards and analysed using ReliaSoft software. The deck machinery considered in this paper is only limited to cranes, winches and capstans since they are the core deck machinery for fishing operations.

Unsudden snapping of the crane and winch tension wires, crane drop load cases, was mentioned as the common random and operational failures by the maintenance crew. The age of the machinery and non-conformity to maintenance set standards was highlighted as the main cause of deck machinery systematic failure.

The available quantitative parameters that were useful to ascertain the condition of the deck machinery included the operational times (average annual operation time), number/times/frequency of breakdowns (annual average failure), number of similar machinery in use/not in operation and the load capacity of the deck machinery as summarized in Table 1.  Deck machinery corrosion, leakages and unwarranted operation noise were some of the evident and observed conditions for some aged/ageing machinery on the fishing vessels (Table 1).

Table 1. Data sample summary for deck machinery.

	Cranes	Winches	Capstans
Sample quantity	12	22	19
Annual average operation time (hours)	4646	4646	4646
Annual aggregate average number of failure	3.8	4.4	2.1
Annual aggregate average number of critical failure	2.3	2.2	1.7
Annual aggregate average number of inspection	18.4	26.8	12.2
Speed (m/min)	–	2–4	5–10
Capacity/load (T)	1–10	5–25	2–10
Motor power (kW)	3–10	10–20	2–5

Data records, based on a five-year horizon, as regards deck machinery maintenance of the fishing vessels were scanty. Annual maintenance data, from which an analysis was done to forecast/predict a five-year operation reliability of the deck machinery, are considered in this paper.

Not all failure records for marine mechanical PME systems and their respective components were available. For cases where the subsystem failure data were not available, system failure data were used. Modern fishing vessels are installed with Computer Maintenance Management Systems (CMMS), where historical data related to maintenance can be easily retrieved. It was observed that even though some fishing vessels do have a CMMS, data related to failure rates, repair times and downtime are not provided for. Such data were obtained from the maintenance crew using a preset datasheet for the maintenance crew to fill in.

From the data collected, fishing vessels have an annual average of 242 operational days as summarised in Table 2. Of the 242 operational days, it is assumed that all plants (P) operate for 100% of the operational days, all machinery (M) operate for an average 80% of the total operational days, while the equipment (E) operate for 70% of operational days, which is in line with Loughran et al. (2002) and Lawrence et al. (2017). The total operational time includes both time at sea and at the docks when the mechanical PME systems are in use.

Table 2. Annual average available operational hours for mechanical PME systems.

	Total annual average available operational days = 242
System	Percentage usage (%)	Annual operational days	Annual operational hours
Plant (P)	100	242	5808
Machinery (M)	80	194	4646
Equipment (E)	70	169	4066

For reliability aspects of the mechanical PME systems of the fishing vessel, the planning horizon is extended for five years as shown in Table 3. This translates into a total operational time of 1210 days, which is equivalent to 29,040 operating hours of the plant (P) for the 5-year planning horizon.

Table 3. Average five-year planning horizon available operational hours for mechanical PME systems.

	Total average five-year planning horizon available operational days = 1210
System	Percentage usage (%)	Five-year operational days	Five-year operational hours
Plant (P)	100	1210	29,040
Machinery (M)	80	968	23,232
Equipment (E)	70	847	20,328

4.2. Results and discussion

4.2.1. Analytical determination of failure distribution

The deck machinery failure data and degradation data were first fitted in Weibull++ using ReliaSoft, from which the best failure distribution that matched the data was selected. For deck machinery failure data, three-parameter (3P) Weibull distribution was ranked number one. For the degradation deck machinery data, the gamma distribution was ranked as 1 followed by the 3P Weibull distribution.

The data fitting of deck machinery for both the failure data and degradation data is in line with Stute et al. (1993), Moore and Perakis (1998), Tyrone (2011), Do et al. (2015) and Wang et al. (2018). A summary of the deck machinery failure data fitting Weibull++ results is indicated in Table 4. This indicates that all beta ($\beta$) values for the deck machinery failure data, irrespective of the type of deck machinery, are more than one but less than 3.5 (i.e. $1<\beta <3.5$). This implies that the deck machinery (cranes, winches and capstans) are in their wear-out phase according to the normal distribution curve (Klutke et al. 2003; Teimouri and Gupta 2013; Sung 2017).

Table 4. Weibull++ results for failure data for deck machinery of ageing fishing vessels.

Parameters	Cranes	Capstans	Winches
Distribution	Weibull 3P	Weibull 3P	Weibull 3P
Analysis	NLRR	NLRR	NLRR
Ranking	MED	MED	MED
Beta	2.542414	1.624096	2.262534
Eta (h)	6710.0413	5527.5609	8278.0216
Gamma (h)	−1664.36	415.72	−2799.08

NLRR, nonlinear rank regression; FM, Fisher’s matrix; MED, median.

For a five-year projected operational period (23,300 h) of deck machinery, Weibull++ results indicate that the earliest time to failure of capstans is 415.72 h, while for both cranes and winches, failure occurred prior to any forecast due to the negative values of the parameter gamma. This implies, keeping other factors constant, capstans have a longer useful lifetime compared to cranes and winches on the ageing fishing vessels. This could be attributed to the fact that capstans have less operational hours during the fishing process as compared to cranes and winches that are often in use.

The Mean Time Before Failure (MTBF) is the time when 63.2% cumulative failures occur for a Weibull exponential distribution (Xu et al. 2014; Haidyrah et al. 2016; Datsiou and Overend 2018). Table 5 shows the Weibull++ analysis results that 63.2% (characteristic life) of the failures of the respective deck machinery occurs in less than one year (4700 h) of operation.

4.2.2. Lifetime parameter estimation for the reliability of ageing deck machinery

Tables 5–7 represent the major reliability lifetime parameters for deck machinery for the ageing fishing vessels. The reliability lifetime parameters in Table 4 are defined for two mission end times of 4700 h (one-year basis) and 23,300 h (five-year basis). At a set mission end time of 4700 h, the reliability of all types of deck machinery on the ageing fishing vessels is less than 52%. The low-reliability values of deck machinery are attributed to the corrective maintenance approach and procedures that are practised on ageing machinery (Pillay et al. 2001; Loughran et al. 2002; Pan and Balakrishnan 2011).

Table 5. Life time parameter estimation for reliability of ageing deck machine.

	Mission end time 4700 h			Mission end time 23,300 h
	Cranes	Capstans	Winches	Cranes	Capstans	Winches
Reliability	0.417205	0.516269	0.44949	5.5014E − 13	0.000043	0.000001
Failure rate (per hours)	0.000349	0.000251	0.000241	0.002875	0.000713	0.001165
MTBF (h)	2865.33	3984.06	4149.38	347.83	1402.52	858.37
Mean remaining life (h)	1995.98	3012.08	2910.52	340.76	1350.46	826.29

Table 6. Reliable life of deck machinery.

	Reliable life (H)
	Cranes	Capstans	Winches
At 85% reliability	1619.26	2221.50	909.09
At 90% reliability	1104.61	1798.55	262.65
At 95% reliability	421.85	1303.45	OUT OF RANGE

Table 7. MTTF of deck machinery.

Parameter	Cranes	Capstans	Winches
MTTF (h)	4291.78	5364.10	4533.33

The remaining useful time for the three categories of the deck machinery is less than the mission end times, an implication that none of the deck machinery categories can serve for 4646 h without failing. An extrapolation for a mission end time of 23 300 h (5 years) depicts the status of the deck machinery in their current state as weary.

A 90% reliability confidence was considered with the upper and lower bounds, as indicated in Table 6. It shows that the reliable life of the respective deck machinery at all reliability levels is still less than one year (4700 h) operational time. This is in agreement with the results shown in Table 5. Among the categories of ageing fishing vessel deck machinery, capstans seem to have a relatively better reliability at all reliability levels, remaining useful life at both mission test end time and mean time to failure (MTTF) as in Table 7.

The B10% values for all categories of the deck machinery are less than the 4700 h as indicated in Figure 3(a) (equivalent to one year of deck machinery operation). The unreliability depicted in Figure 3(a) shows that the unreliability rate of capstans is increasing at a decreasing rate compared to cranes and winches. Maintenance should be emphasised for ageing cranes and winches. If the current preventive maintenance schedules for cranes and winches are increased by 30%, then this might avert the reliability uncertainties of these two categories of deck machinery (TSB-M16A0115, 2017).

Figure 3. Deck machinery reliability plot.

Also depicted in Figure 3(b–d), the reliability, failure rate and failure distribution of deck machinery of ageing fishing vessels for a forecasted one-year (4700 h) and five-year (23,300 h) operation is well below the prescribed reliability standards for mechanical systems (OREDA 2009; Tyrone 2011).

4.2.3. Degradation analysis of deck cranes

Of the categories of deck machinery, degradation analysis was only done for cranes due to the availability of degradation data for some fishing vessels regarding cranes. On average, the design load for the cranes of the ageing fishing vessel covered in this paper is 3.5 tonnes. Thus using the dropped load criteria, a critical load of 3 tonnes is considered (BAE Systems Ltd 2002). A five-year operation time (260 weeks) for the deck cranes is considered for analysis. Of the five sampled cranes for degradation analysis, as indicated in Figure 4, only two cranes pass the critical load test for both the set end mission times of 52 and 260 weeks. This is best explained by the fact that these two cranes that passed the degradation analysis critical load test were on fishing vessels where deck machinery maintenance was in order.

Figure 4. Degradation of deck crane.

For comparison purposes for prediction of failure time for deck machinery, results of a box plot in Figure 5 are consistent with the Weibull results. The failure data for winches and capstans from the box plots areare relatively uniformly distributed as compared to that of cranes. Capstans have a higher median and maxima useful remaining lifetime compared to cranes and winches.

Figure 5. Boxplot of failure for the predicted mean URL.

4.2.4. Reliability prediction

A reliability standard Naval Surface Warfare Center (NSWC-11) handbook is used to predict the failure rates of deck crane constituent mechanical components using the crane block diagram. NSWC-11 was used as a benchmark for reliability prediction and maintainability characteristics for mechanical systems for comparison purposes. The NSWC-11 is the latest handbook that is seemingly and generally accepted in the reliability and maintainability aspects for mechanical machinery in the marine industry (Catelani et al. 2015; Vedachalam et al. 2015; Vedachalam and Ramadass 2017; Yasseri and Baha 2018). FIDES and MIL HDBK 217F (Military Handbook -217F) are reliability standard guides for electronic components and systems. The OREDA (Onshore/Offshore Reliability Data) handbook is an alternative to the NSWC handbook that is used for reliability aspects for machinery in the oil and gas industry. IEEE 493 (Institute of Electrical and Electronics Engineers -493) is a recommended practice for the design of reliable industrial and commercial power systems (Vedachalam et al. 2014).

Much as there could be variations in maintenance approaches in different regions/cultures, NSWC-11 provides a better basis for predicting the reliability of mechanical systems where physical failure data are not readily available or have missing gap. Specific reliability and maintenance data for the particular deck machinery and their components would be of best use in the reliability analysis, but this was not the case for some aged fishing vessels. The ReliaSoft software used for reliability analysis in this paper incorporates NSWC general data for mechanical systems.

NSWC (a) are reliability prediction results using the NSWC-11 standard for mechanical systems without incorporating the field data for deck machinery, while NSWC (b) incorporates the deck crane field data. It should be noted that NWSC-11 standard for reliability prediction does not incorporate the actual operating conditions, environmental conditions and specifications of the mechanical systems. Reliability prediction results in Table 8 show that for ageing fishing vessels, the turret and crane rollers have a higher failure rate contribution of between 74% and 85% for NSWC (a), and between 23% and 77% for NSWC (b) compared to the overall failure of the deck cranes. Preference should be given to gear rotating mechanism (turret) and crane rollers in maintenance inspections and schedules.

Table 8. Reliability prediction for deck crane components.

		Failure rate × 10^−6 (t = $\mathrm{\infty }$)		MTBF (H)		Contribution
Name	Quantity	NSWC (a)	NSWC (b)	NSWC (a)	NSWC (b)	NSWC (a)	NSWC (b)
Crane	1	512.1004	413.167	1950	2420	1.0000	1.0000
Jib/boom	1	74.3882	95.1443	13,443	10,510	0.1467	0.2303
Hydraulic cylinder	2	9.0720	11.3456	110,230	88,140	0.1220	0.1192
Crane pulley/roller	2	65.0051	72.4566	15,383	13,801	0.8739	0.7615
Tension wire	2	0.3112	11.3421	3,213,400	88,167	0.0042	0.1192
Pedal – stand	1	432.7122	318.0227	2.311	3144	0.8533	0.7697
Crane pulley/roller	2	65.0051	72.4566	15,383	13,801	0.1502	0.2278
Turret	1	321.1080	142.3102	3114	7027	0.7421	0.4475
Tension wire	3	2.16110	11.3421	462,730	88,167	0.0050	0.0357
Crane motor	2	40.0000	63.3467	25,000	15,786	0.0924	0.1992
Brake system	1	4.4381	28.5671	225,320	35,005	0.0103	0.0898

To refine the reliability prediction results, field/environment conditions and specifications of the crane deck machinery were later integrated into NSWC-11 to obtain NSWC (b) results. This included the number of field failures, failure rates, temperature, humidity, maintenance time, elasticity and hydraulic properties of the crane. It should be noted that since the average age of the sampled vessels is 25+ years, some of the technical specifications of the crane components were not available. To overcome some of the shortfalls associated with field data, specifications of similar components for the deck cranes operating in the same conditions were used.¹ Using the field data, we can conclude that the overall crane failure rate for NSWC (b) is lower than that of NSWC (a). The MTBF of 2420 h for NSWC (b) is relatively higher than 1950 h for NSWC (a), though slightly lower than the Weibull++ MTBF value of 2865 h in Table 4(a). This implies that it takes approximately 3.98 months using Weibull++ results and 3.36 months using NSWC (b) prediction results for the deck crane to fail.

5. Conclusion

The reliability and degradation of marine deck machinery was analysed using Weibull and Gamma distributions, respectively as methods that are widely accepted in estimating the reliability and degradation parameters for further analysis.

5.1. Observations

It is observed that in ageing fishing vessels, novel maintenance approaches like condition-based maintenance are not applied. This is attributed to the cost required to upgrade the deck machinery, which seems not to make economic sense to the vessel owners/operators. Deck machinery gets far less maintenance attention than engine systems and other auxiliary machinery on the vessel. As described in Section 2, deck machinery is key on a fishing vessel since much of the fishing operations at sea are done by the deck machinery.

Keeping other factors constant, capstans have a longer useful lifetime compared to cranes and winches on the ageing fishing vessels. This could be attributed to the fact that capstans have less operational hours during the fishing process as compared to cranes and winches that are often in use.

5.2. Recommendations

The maintenance of deck machinery systems beyond their useful lifetime is paramount in order to ensure the safety of the operators and keep downtime at its optimal base. A re-maintenance plan/strategy is recommended where the vessel owners/managers should emphasise scheduled maintenance checks and reports from the maintenance crew on a weekly basis. For reliability prediction of ageing deck machinery, degradation data such as crane drop load, crane load capacity, winch loads, winch load tension, capstan rpm, crane/capstan brake power among others, ought to be used in preference to only failure data because degradation can be modelled using several approaches. These degradation data trends can be used by the maintenance crew to further analyse machinery performance and maintenance options. When using reliability software, field data and machine specifications ought to be incorporated for best interpretation of the software output results. There is a need to carry out fatigue tests on aged/ageing deck machinery in order to ascertain the fatigue-failure relationship and how fatigue affects the reliability of such machinery.

5.3. Further research

It is observed that deck machinery maintenance procedure manuals do not incorporate the maintenance during the wear-out/degradation phase. There is still an information gap as to why the deck machinery manufacturers tend to negate this critical aspect. Since mechanical systems degrade as they age, there is no clear outlook/studies regarding the threshold value(s) of various mechanical systems. The accuracy of reliability assessment highly depends on the quality of both maintenance and degradation data, which is not readily available in most cases for ageing machinery. Failure data for ageing systems should be component based rather than system based and a number of degradation tests for specified durations should be carried out. It’s thought that empirical analysis ought to be carried out for both reliability and degradation of deck machinery of ageing systems and the results are compared with the analytical model results.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

The authors gratefully acknowledge African Development Bank (AfDB) HEST Uganda (2016) chapter for providing funds for the research.

Notes

1 https://www.mhi-mme.com/products/deckcrane