Risk assessment: How do I weight manufacturing hazards that I’ve found?
The risk involved with a given machine or process depends on what bad things can happen, and how likely they are. This risk analysis tutorial explains how to weight hazards.
As engineers, we are trained to design systems based on quantitative physical models based on careful measurements. We are, however, often faced with situations where quantitative data is unavailable and measurements are difficult or impossible to make. Risk assessment provides a prime example of this difficulty. Of course, the fact that risk analysis is a critical component of what we do just makes the situation harder.
Conceptually, risk assessment is quite simple, as the accompanying sidebar (See Quantitative risk assessment, below) shows. If there are a given number of ways the system can fail, and each failure has foreseeable consequences, it’s a simple matter of forming a vector representing the failure consequences, and another representing the probabilities of the failure modes, and taking the scalar product of the two vectors.
The devil, as usual, is in the details. First, the consequences of each failure mode would have to be quantified. Since the severity of these consequences is highly subjective, assigning a quantitative value to its severity is difficult, to say the least.
Henry Ford has been apocryphally quoted as once saying: “If you don’t break a few axles once in a while, you’re making them too strong.”
Motorists stranded on dirt roads in the Mojave Desert when the axles on their Model T Fords broke were known to take issue with this statement.
Henry Ford supposedly put a low value on the consequences of Model T axle failures. The stranded motorists put it much, much higher.
Probabilities are much easier to quantify reliably. Just keep records of all the failures, and divide the number of failures by a usage measure. Suppose you record 525 axle failures on Model Ts that ran a total of 750,000 miles. You could easily calculate that the axle-failure probability was 0.07 percent per vehicle mile, or 700 ppm. If you decide that’s just not acceptable, and improve Model T axle strength, however, it won’t be of much comfort to the 525 motorists whose axles broke while you collected data.
Five standards, promulgated by different organizations provide guidance on methods by which engineers can qualitatively classify risks:
- ANSI B11-TR3-2000 – Technical Report; Risk Assessment and Risk Reduction – A Guide to Estimate, Evaluate and Reduce Risk Associated with Machine Tools
- ANSI/RIA R15.06-1999 – American National Standard for Industrial Robots and Robot Systems – Safety Requirements
- ISO 12100-1:2003 – Safety of Machinery – Basic concepts, general principles for design – Part 1: Basic terminology, methodology
- ISO 14121–1:1999(E) – Safety of Machinery – Principles of risk assessment
- EN 954-1 – European Standard – Safety of Machinery
ANSI B11.TR3-2000 provides the decision matrix shown in Table Estimating the Level of Risk. This table divides the severity of harm into four categories, and provides four probability levels as well. At each row/column intersection, it provides a category of risk running from negligible to high.
While these standards provide a method for assessing risk, they do not provide the knowledge required to make the assessment. That is, the knowledge needed to say that a particular failure mode falls into the “Moderate” severity of harm column, or which probability row to assign it to. That knowledge must come from outside the standard. You get it from well informed engineers having expertise with both risk assessment in general, and the particular type of equipment involved.
Some companies have that expertise in house. The manufacturer of a bottle-capping machine, for example, should have the expertise to list the possible failure modes associated with that machine, as well as their severity. They should also be able to assess the probability of that failure in their test cell under their final-test conditions.
They cannot, however, be expected to know how probable a failure might be on your production floor. It will be different. It might be higher, or lower, but it will be different. There are just too many unknowns for them to predict, such as:
- What guards will be installed around the machine?
- How well trained are your operators and maintenance personnel?
- How closely does your management supervise operation and maintenance activities to ensure that guards are kept in place, and interlocks are not bypassed?
- How well maintained will the equipment be to ensure that safety systems are kept at optimum performance?
While risk assessments can, and should, be made during the system’s design and fabrication, there should be another risk assessment made upon system installation. Assessments should be renewed on a regular basis, such as annually, throughout the system’s life, because its condition will change with age, and failure probabilities will change as well.
While the equipment manufacturer should provide initial risk assessment for the system, and customer companies should insist that a risk report be delivered as part of the equipment’s documentation, it is up to the customer to assess risk in the final production environment. Again, some companies have this expertise in house. This in-house expertise, however, usually is in engineering groups that are tasked with other time-consuming jobs as well. Many companies simply do not have this in-house exprtise.
Thus, it is often wise for automation customers to go to third-party consultants to conduct risk assessments. Some standards and regulatory agencies provide guidance – in the form of lists of qualified risk assessment professionals – to whom automation customers can turn for help. They will not, however, provide actual recommendations. It is up to the customer to choose a risk assessment consultant through their usual supplier-vetting process.
Severity of harm
Probability of occurrence of harm
Quantitative risk assessment
Risks involved with a particular automatic machine installation arise from a number (N) of possible events. Conceptually, the analysis follows the convolution model shown in equation 1:
R = a1P1 + a2P2 + … + aNPN, 
where R is the total risk, ai is a weighting factor quantifying the severity of the damage incurred if the ith event actually happens. The Pi values represent the probability that the ith event will happen. The ai values, once set, are consistent throughout the system’s life, but the Pi values change as things like guarding, physical location, etc. change.
The difficulty arises because assigning ai values is highly subjective, and evaluating the probabilities takes a great deal of empirical data, which may or may not be available. Recognizing these difficulties, current standards do not require an actual quantitative risk assessment. Instead, they provide guidance to mitigate risk to acceptable levels in a qualitative way.
Scott Krumwiede, manager, RWD Technologies, contributed to this tutorial.
For more on equipment safety, visit the Siemens Website at www.sea.siemens.com/safety.
For more on risk assessments, visit the RWD Technologies Website at www.rwd.com.
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