Human error plays a pivotal rule in all aspects of engineering activities such as operation, maintenance, design, inspection and installation. Industries are faced up to various significant human errors and consequently irrecoverable loss each year, but still there is a lack of heeds to qualify as well as quantify such errors. This paper tries to estimate the probability of failure in lifting of light structures in sea by considering human errors. To do this, a strong qualifying tool such as Functional Resonance Analysis Method (FRAM) is applied to develop high risk accident scenario by considering non-linear socio-technical interaction in system. Afterwards, human error probability is calculated for each activity using the Success Likelihood Index Method (SLIM) based on resonance that is carried out in FRAM network. Then Event Tree (ET) is conducted to assess consequences. The present study is aimed to interpret the importance of attentions to qualitative methods in implementing quantitative risk analyses to consider human error in calculation. The final outcome depicts that considering human error in the process of risk assessment will result in more accuracy and reliability in final Risk Probability Number (RPN). The developed methodology has been applied to a case study of an offshore installation.
Risk assessment is a systematic process for analyzing and evaluating events that could affect the achievement of objectives, positively or negatively. Such events can be identified in the external environment and within an organization’s internal environment. It is necessary to define both risk analyzing and risk evaluating to represent the risk assessment clearly. Risk analyzing is therefore defined as systematic use of available information to identify hazards and to estimate the risk to individuals, property, and the environment [
The risk analysis may be qualitative or quantitative, depending on the objective of the analysis. Qualitative risk analysis is a risk analysis where probabilities and consequences are determined purely qualitatively. Qualitative assessments are the most basic form of risk assessment, categorizing potential risks based on either nominal or ordinal scales. Whereas quantitative risk analysis (QRA) is determined as risk analysis that provides numerical estimates for probabilities and/or consequences―sometimes along with associated uncertainties. A QRA is best suited for quantifying risk associated with low-probability and high-consequence events, and may range from specialized probabilistic assessment to large-scale analysis. The term semi-quantitative risk analysis is sometimes used to denote risk analyses that quantify probabilities and consequences approximately within ranges.
As part of these assessments, risk and reliability analysts are required to perform evaluations of human reliability in addition to the analyses of hardware systems which are the primary focus of a typical risk assessment. Increasing emphasis is being placed on a comprehensive assessment of the human role in system safety following the occurrence of major disasters in the petrochemical industry such as Piper Alpha and other industries like Chernobyl where human errors are seen as direct or indirect causes. A better estimate of human reliability would help to design more effective safety systems and evaluate more accurate risk assessments. Human reliability is the probability that a person correctly performs system-required activities in a required time period (if time is a limiting factor) [
The Functional Resonance Accident Model and Functional Resonance Analysis Method [
It should be noted that since all operation are assumed to be performed at same time, it is not possible to consider the variability and resonance of all functions in an entire accident scenario. Hence, the risk assessment is conducted for an operation as a specific resonance in the FRAM network with considering the human errors. This resonance is based on variabilities of the functions in the case study of the paper.
Quantifying the error potential of the actions is provided by an analytical approach: Success Likelihood Index Methodology (SLIM) [
A risk-based methodology is developed to assess the risk of studied operation as illustrated in
Task Analysis (TA) is a fundamental methodology in the assessment and reduction of human error and their relative risk. TA methods can be used to eliminate the preconditions that give rise to errors before they occur. They can be used as an aid in the design stage of a new system, or the modification of an existing system. Task
analysis can also be used in a retrospective mode during the detailed investigation of major incidents. The starting point of such an investigation must be the systematic description of the way in which the task was actually carried out when the incident occurred [
When the scenario is developed, the human related activities and the probability of error for each activity is identified. A summarized representation for different activities and tasks for lifting operation procedure are presented in
1. Draw up work 1.1 Planning work order 1.2 Conducting work permit system 1.3 Equipment diagnostics 1.4 Risk assessment of each planned activity 1.5 Tool box meeting before startup of each work 1.6 Documenting the permit to work system |
---|
2. Mobilization 2.1 Crane sea-fastening 2.2 Survey equipment installation 2.3 ROV system installation 2.4 Providing side effecting equipment |
3. Positioning the vessel in the site 3.1 Apply GPS and thrusters to being stable in the site |
4. Startup survey system 4.1 Check beacon, multi beam, USBL to ensure that they work 4.2 Property connect the beacon and underwater gyro cable to the load |
5. Prepare lifting equipment 5.1 Connect properly wire and belts to the load 5.2 Check if wires/belts are out of order or not 5.3 Check the safety factors and breaking load for wires and working load for belts |
6. Lower support down 6.1 Check the speed of lowering process specially near the surface |
7. Check the position of support by survey team 7.1 Monitor the load transitional and rotational position in the sea |
8. Check the position of support by ROV team 8.1 ROV takes the fix point for validation of the load position |
9. Release the support in the seabed 9.1 Check the position, if it is ok release the load |
The Functional Resonance Accident Model and its associated Functional Resonance Analysis Method (FRAM; [
FRAM is based on four principles [
The FRAM network of studied operation is presented (
As it is obvious from FRAM network the process has 14 functions, 7 background and 7 foreground. The functions are coupled with each other via their common aspects. There are some functions with barrier goal such as quality control, winch control and Connecting Wire/Belts and inspection of connection. Lack of functional barriers make some functions of the operation such as Under Water Gyro/Beacon, USBL system, Lifting support by Crane, vulnerable against unpredictable variabilities.
It should be noted that since all operation are assumed to be performed at same time, it is not possible to consider the variability and resonance of all functions in an entire accident scenario. Hence, the risk assessment is conducted for an operation as a specific resonance in the FRAM network with considering the human errors. This resonance is based on variabilities of the functions (it is specified by numbers). The resonance is a detectable signal that emerges from the unintended interaction of the variabilities of many functions that together may combine in unexpected ways, leading to consequence that are disproportionally large [
After developing a high-risk accident scenario qualitatively based on FRAM, it is needed to find the probability of error for each human related lifting activity which is one of the main parts of consequences in event tree for risk assessment. Without estimating human error probability (HEP) the final failure estimation for top event in event tree will not be plausible. Since the majority of errors during the operation are due to human errors. Different activities and tasks are identified for this purpose in
The SLIM integrates various Performance Shaping Factors (PSFs) relevant to a task into a single number called a success likelihood index (SLI). The SLI is calculated by the following formula (see Equation (1)). For numerous sub-activities for each task then SLI should be calculated for each sub-activity separately and consequently the related HEP should be calculated by Equation (2) in which, “n” is the number of sub-activity and “m” is the number of PSFs to find related SLI for task jth, besides, R and W are the Rate and Weight of each PSF respectively.
S L I = ∑ i = 1 m R i W i (1)
S L I j = ∑ j = 1 n ∑ i = 1 m R i j W i . (2)
For a given SLI, the human-error probability (HEP) for a task is estimated by using the Equation (3):
L o g ( H E P ) = a × S L I + b (3)
H E P = 10 a × S L I + b (4)
where a and b are constants determined from two or more tasks for which HEPs are known. In this study a and b are considered as −1.95 and 10 E−04, respectively.
Identifying PSFs is a substantial step of presenting the SLIM. The first step of Human Reliability Assessment is to focus on human behavior and identify a set of human factors believed to be related to performance. These PSFs are then employed to estimate the probability of human error in a given situation [
Performance shaping factor is provided basis for considering potential influences on human performance and systematically considering them in quantification of Human Error Probabilities (HEPs). PSFs often characterized as internal and external. Internal PSFs are influences that the individual brings to the situation such as mood, fitness, stress level, etc. External PSFs are influences in the situation or environment that affect the individual such as temperature, noise, work practices, etc. Currently there is no standard set of PSFs used in HRA methods, but most sets use PSFs identified in human performance literature [
Determining the weight of PSFs to estimate the SLIs is one of the most pivotal steps. Human performance data with greater detail is difficult to find in real world situations, which requires the use of expert judgment techniques [
Rating the PSFs is another important step in the SLIM procedure. Participant experts such as technical engineers select rating R from 0 to 1 for each of PSFs. Each PSF rating has an ideal value of 1 at which human performance is judged to be optimal. These ratings are based on six PSFs demonstrated in
PSF | Rank | Weight |
---|---|---|
Experience | 10 | 0.21 |
Skill | 9 | 0.19 |
Motivation | 8 | 0.17 |
Stress level | 7 | 0.15 |
Work Memory | 7 | 0.15 |
Time pressure | 6 | 0.13 |
such technologies is the lack of knowledge with regard to inappropriate or missing experimental and operational data. As a result, a combination of qualitative and quantitative risk assessment with expert judgment could result in a better interpretation of system based on epistemic knowledge and subsequently a better ability to cope with scarce in operational experience and uncertainty.
By applying Equation (2) SLI were obtained for each activity. Afterward, Equation (3) and Equation (4) are used to calculate the HEP of each task. Human Error Probability of activities is presented in
It is obvious that the first top event for related ET is related to human error. So, it is necessary to compute the cumulative probability of the first top event, Human error, using the probabilities of the sub-activities based on Equation (4). Assuming that these activities and sub-activities are independent to find the worst-case scenario (The reason is that the independency indicates that the output of following event occurs if any of the input sub- events occur), then, the probability of human error, HEPT, for light structure’s lifting in the offshore industry can be calculated using Equation (5).
H E P T = 1 − ∏ j = 1 n ( 1 − H E P j ) . (5)
Activity | HEP | Uncertainty | |
---|---|---|---|
Lower bound | Upper bound | ||
1. Draw up work 1.1 Planning work order | 9.73E−02 | 6.21E−02 | 1.53E−01 |
1.2 Conducting work permit system | 6.56E−02 | 3.81E−02 | 1.23E−01 |
1.3 Equipment diagnostics | 7.24E−02 | 4.62E−02 | 1.23E−01 |
1.4 Risk assessment of each planned activity | 7.21E−02 | 5.51E−02 | 1.13E−01 |
1.5 Tool box meeting before startup of each work | 9.88E−02 | 6.3E−02 | 1.32E−01 |
1.6 Documenting the permit to work system | 8.4E−02 | 5.36E−02 | 1.44E−01 |
2. Mobilization 2.1 Crane sea-fastening | 9.03E−02 | 5.34E−02 | 1.55E−01 |
2.2 Survey Equipment Installation | 9.48E−02 | 6.05E−02 | 1.48E−01 |
2.3 ROV system Installation | 8.87E−02 | 5.15E−02 | 1.52E−01 |
2.4 Providing side effecting equipment | 1.08E−01 | 6.12E−02 | 1.79E−01 |
3. Positioning the vessel in the site 3.1 Apply GPS and thrusters to being stable in the site | 1.05E−01 | 5.71E−02 | 2.11E−01 |
4. Start-up survey system 4.1 Check beacon, multi beam, USBL to ensure that they work property | 9.71E−02 | 5.23E−02 | 1.92E−01 |
4.2 Connect the beacon and underwater gyro cable to the load | 9.92E−02 | 6.77E−02 | 2.11E−01 |
5. Prepare Lifting Equipment 5.1 Connect properly wire and belts to the load | 8.18E−02 | 4.75E−02 | 1.46E−01 |
5.2 Check if wires/belts are out of order or not | 1.01E−01 | 6.08E−02 | 1.58E−01 |
5.3 Check the safety factors and breaking load for wires and working load for belts | 8.00E−02 | 5.24E−02 | 1.82E−01 |
6. Lower support down 6.1 Check the speed of lowering process specially near the surface | 1.27E−01 | 6.11E−02 | 2.11E−01 |
7. Check position of support by Survey Team 7.1 Monitor the load transitional and rotational position in the sea | 1.09E−01 | 5.15E−02 | 1.78E−01 |
8. Check position of support by ROV team 8.1 ROV takes the fix point for validation of the load position | 1.09E−01 | 5.15E−02 | 1.78E−01 |
9. Release the support on seabed 9.1 Check the position, if it is ok release the load | 1.36E−01 | 6.99E−02 | 2.18E−01 |
Applying a continuous improvement in the system Safety Management in any kinds of process in the industries needs different approach such as Risk Assessment. The expected achievements of risk Management is that the hazards and risks of the system were identified, analyzed, assessed, evaluated, controlled and finally reduced. In the assessment phase, different approaches are used such as Quantitative Risk Assessment (QRA) and probabilistic safety assessment (PSA). Among several techniques available as a Quantitative Risk Assessment (QRA), Event Tree (ETs) have widely been used to explore the probability of consequences resulted from an initiating event. It has been used successfully in the nuclear industry, the chemical process industry, and in several other application areas. Event tree analysis is also commonly used for human reliability assessment. The method is inductive and follows a forward logic [
In probability theory and statistics, the Poisson distribution is a discrete probability distribution that expresses the probability of a given number of events. The occurrences of the hazardous event are often modeled by a homogeneous Poisson process with frequency of failure rate λ , which is the expected number of occurrences per year (or some other time unit).
We use Poisson distribution to find probabilities of occurrences of hazard-promoting factors sequences. The failure rate estimates are based on Recorded failure events, Expert judgment and Laboratory testing and in some cases a combination of these. The HPP of hazardous events of the event tree in
Pr ( N ( t ) = n ) = ( λ t ) n n ! e − λ t for n = 0 , 1 , 2 , ⋯ (6)
It should be noted that since all operation are assumed to be performed at same time, it is not possible to consider the variability and resonance of all functions in an entire accident scenario. Hence, the risk assessment is conducted for an operation as a specific resonance in the FRAM network with considering the human errors. Based on accident reports, related research and expert opinions, the most probable accident scenario is the provided resonance in the FRAM network in which a human error may lead to inappropriate checking of the connection between belt and trunnion. Besides inadequate control in the speed of the load in lowering process, specially near the surface may increase the probability of failure that is slamming of the load and consequently disconnection of load and derrick. The resulting diagram displays the mentioned accident scenarios (see
Applying the probabilities of hazard-promoting factors, the probabilities of consequences can be calculated for light lifting in the sea (
By working through the entire event tree, we produce a spectrum of light lifting failure and their probability for the various accident sequences (
Hazard-promoting factors | Probability |
---|---|
Human error (HEPT) | 0.86 |
Improper connection between belt and trunnion | 0.08 |
Lack of speed control in lowering process | 0.11 |
Slamming | 0.10 |
Disconnection | 0.12 |
Index | End state description | Probability |
---|---|---|
C1 | Safe condition | 1.4E−01 |
C2 | Mishap | 7.91E−01 |
C3 | Near miss | 6.1E−02 |
C4 | Improper fastening of connection points like shackles, belts or slings; successful installation; without damage | 6.81E−03 |
C5 | Slamming; successful installation; minor property damage | 6.65E−04 |
C6 | Disconnection; unsuccessful installation; major property damage; major injury | 9.08E−05 |
factors reached a pick in human error. It depicts two matters, firstly the importance of human error rule and secondly is that without considering human error in the processing of light lifting risk assessment there is a striking difference in the final outcomes.
A risk-based approach has been conducted to assess the risks of light lifting in offshore industry. This methodology consists of Task Analysis, Qualitative Risk Assessment and Quantitative Risk Assessment. The aim is to illustrate the importance of the role of human error in evaluating and estimating the risks of system with pin- point accuracy. To do so, risk assessment in this study is divided into two parts: Qualitative Risk Assessment and Quantitative Risk Assessment. The HEPs are estimated by applying the SLIM process. The probability of other hazard-promoting events is calculated by using Homogeneous Poisson Process (HPP). Although quantitative risk assessment and probabilistic safety analyzing are vital and necessary for system safety management, the study showed that by eliminating the role of human reliability in the assessment, the effectiveness and accuracy of evaluation will decline enormously. After obtaining the HEPs based on a specific scenario, the final value of the risk is calculated by integrating the HEPs and consequence analysis results (Risk Assessment). The main challenges experienced for this study are data scarcity and uncertainty in expert opinion. By applying multi-ex- pert knowledge, this paper presents a methodology to overcome these two major limitations.