Fleet Management is changing today with the advent of satellite links to data historians.
Fleet Management is changing today with the advent of satellite links to data historians.
Key Performance Indicators (KPI’s) are an important management tool to measure operations performance, and are often used to measure maintenance. Unfortunately, unlike operations, there are only a few real measures of maintenance that adds value to the operations.
The problem is some of the measurements that are used are often easy to manipulate and have no real value added. Key Performance Indicators (KPI’s) are a small number of agreed-upon measurements that reflect your organization’s critical goals for success — a numerical snapshot in time. For maintenance KPI’s to be really effective they must be aligned with operating KPI’s and there objectives.
Remember, KPI’s should only be one measurement technique in your arsenal. They can be a quick and useful tool to diagnose strengths and weaknesses in your process, make strategic decisions, and ensure you are heading in the right direction. The real benefit is in the discussion of results with your team members, not the numbers themselves.
The Seven Steps to Alarm Management
Step 1: Create and Adopt an Alarm Philosophy
A comprehensive design and guideline document that makes it clear “exactly how to do alarms right.”
Step 2: Alarm Performance Benchmarking
Analyze the alarm system to determine its strengths and deficiencies, and effectively map out a practical solution to improve it.
Step 3: “Bad Actor” Alarm Resolution
From experience, it is known that around half of the entire alarm load usually comes from a relatively few alarms. The methods for making them work properly are documented, and can be applied with minimum effort and maximum performance improvement.
Step 4: Alarm Documentation and Rationalization (D&R)
A full overhaul of the alarm system to ensure that each alarm complies with the alarm philosophy and the principles of good alarm management.
Step 5: Alarm System Audit and Enforcement
DCS alarm systems are notoriously easy to change and generally lack proper security. Methods are needed to ensure that the alarm system does not drift from its rationalized state.
Step 6: Real-Time Alarm Management
More advanced alarm management techniques are often needed to ensure that the alarm system properly supports, rather than hinders, the operator in all operating scenarios. These include Alarm Shelving, State-Based Alarming, and Alarm Flood Suppression technologies.
Step 7: Control and Maintain Alarm System Performance
Proper management of change and longer term analysis and KPI monitoring are needed, to ensure that the gains that have been achieved from performing the steps above do not dwindle away over time. Otherwise they will; the principle of “entropy” definitely applies to an alarm system.
Reliability Centered Maintenance is a great approach to optimizing equipment performance and reliability. Its conception came from tragedy in the aviation industry that resulted in loss of life. After decades of reliability research in aircraft safety and equipment failure, the RCM process was born. Today, RCM2 is used in other industries worldwide but not to the same extent. Why? One of the challenges for implementing an RCM process is the cost and time it takes to implement.
The initial process requires lots of resources or subject matter experts (SME) from operations, engineering and maintenance to review the operating context of the equipment being analyzed. A complex RCM analysis takes weeks to define the functional failure modes and effects, write and implement the preventive maintenance (PM’s) after the analysis is complete. Unfortunately some organizations do not have sufficient resources at its disposal for this type of commitment.
To aid the SME’s and minimize their time together as a collective group, you should assemble a package for each participant in the analysis. Some examples of what I include are: written operating context, data from your EAM/CMMS system (including maintenance plans and calendar or hourly based scheduled preventative maintenance (PM)), any known failure frequencies, location and equipment hierarchy, type taxonomies, maintenance history, P&ID, applicable drawings that show ancillary equipment which may need to be added to this RCM or have a standalone analysis.
The net result of this analysis will provide an accurate failure frequency and failure codes that will greatly improve your organizations overall maintenance strategies. Where most companies fail is the implementation of the process. The reason for this is the process itself is intended to be dynamic not static. Any dynamic process needs to have some form of feedback. The RCM process is no exception and any change in the operating context or equipment change requires the SME’s to revisit the RCM analysis for changes. This feedback loop is difficult to monitor and requires notification to the RCM facilitator to ensure its long term success.
A proactive maintenance management team would also consider some predictive analytics or condition based maintenance when implementing the RCM’s. I recommend utilizing an OSIsoft PI system in conjunction with a remote diagnostic center to mitigate intrusive maintenance. Utilizing a non-intrusive maintenance strategy for implementing the RCM process will greatly reduce unscheduled shut downs and equipment failures.
So where should you apply Reliability Centered Maintenance in your organization? Well there is no short answer but the best rule of thumb or industry best practices are simply where you need it. A great example on where to start is meeting regulatory compliance. Most industries today are trying to comply with some level of regulatory compliance where the fines for a violation could be costly for their company.
Another example could be simply what equipment in your plant or facility needs the highest amount of reliability? By starting with the lowest hanging fruit, your organization will see the return on investment (ROI) that will offset the time and resources it took to implement a sustainable RCM2 process.
Remote operation and monitoring of equipment via an integrated SCADA or DCS system is a fundamental concept of remote diagnostics. Historically most companies utilize their Control Center to manage all alarms and diagnostics.
Unfortunately, most control centers are not staffed with personnel knowledgeable in equipment diagnostics as they are typically staffed with operations centric personnel dedicated for command and control.
New equipment today can produce large amounts of information about the health/condition of equipment. For many organizations and departments it is not clearly understood how this vast amount of data can be managed and who has the accountability to take action.
This is why companies today are developing standalone Maintenance & Diagnostics Centers to fill this gap. This allows a small team of SME’s to fully leverage the telemetry from these automated systems and gain meaningful actionable insight into the health of their assets.
Initially, most organizations quickly see returns on their investment by increasing the time between failures and mitigating unscheduled shutdowns of equipment. Where most organizations fail today is the politics around deploying a centralized diagnostic center. Typically, this happens by not clearly articulating the different roles and responsibilities of the control center and the diagnostic center.
While this seems very fundamental it is the leading contributor to diagnostic centers failure. Typically organizations are so focused on solving a technical issue or overcoming a resource gap that they miss the internal political aspects of deploying a diagnostic center.
One of the rubs that operations and maintenance will have to overcome is alarm management. While both organizations need to share accountability on some events and alarms their actions are very different.
While most Gas & Liquid pipelines have adopted an alarm management philosophy to meet regulatory requirements they are the exception to the rule. A common alarm strategy is called a Documentation and Rationalization or D&R. This process forces maintenance and operations to assign and prioritize alarms. Typically, alarms are assigned to operations and events are assigned to maintenance and/or engineering.
In today’s regulatory environment companies need to move past traditional Alarm Management and evolve to true Data Management as its core concept in managing automated/remotely operated equipment. Having clarity on roles will help an organization transition effectively and operate a highly reliable system while maintenance & engineering actively monitoring equipment at facilities for potential failures or shutdowns.
Today’s technologies have opened the door to better equipment performance with the advent of data historians, better networks, faster processor speeds, and advanced algorithms. By integrating these technologies any company can leverage and monitor data more efficiently from a centralized location while increasing reliability and system integrity.
With the advent of these technologies traditional calendar or hourly-based maintenance can be a wasteful practice. Seldom does a device or equipment actually require maintenance during a scheduled PM, thus making scheduled maintenance a wasted practice in many cases. During intrusive preventative maintenance failure catalysts are often introduced into properly working equipment resulting in shut downs and equipment damage.
Many companies have developed some of these tools to not only reduce this type of intrusive and costly maintenance, but to perform this type of maintenance in much more efficient and cost effective manner.
Adding condition based maintenance to your strategy as a maintenance practices will free up resources for corrective maintenance, training, etc. Automating Preventative Maintenance PM’s has clear measureable return on investments. Monitoring CBM
S from a centralized diagnostic center augments your engineering and maintenance work force by levering current and historical data remotely.
Most maintenance teams start with a pilot program on what they consider to be the low hanging fruit. The next logical step for most companies is to develop a diagnostic center once the effectiveness of CBM’s has been validated.
Here are a few things to consider when developing a CBM strategy:
• Monitor and respond to maintenance type events or alarms.
• Assist Maintenance teams in trouble shooting locally or in the field.
• Perform initial equipment diagnostics for Operations, Engineering, Maintenance, or other SMEs as needed.
• Free up resources
• To assist with troubleshooting and validating corrective actions.
• To identify issues which may lead unintended shutdowns or equipment outages
• Perform post mortem or root cause analysis on equipment failures
• Achieve Reliability Centered Maintenance
• Ensure regulatory compliance.
• To inform and support management decisions
• Align with organizations objectives
What are Predictive Analytic and predictive modeling and how can industries benefit from them today? While both of these techniques can be applied in many different types of analysis our discussion will be around the benefits for plant and facility maintenance uses. First, let’s talk about what the differences are.
Predictive analytic uses a variety of statistical techniques from equipment modeling to data mining so that current and historical data can be analyzed to make predictions about future equipment events or failures.
Predictive modeling leverages patterns found in current and historical data to identify risks and opportunities. Models capture relationships among many factors to allow assessment of risk or potential failure associated with a particular set of conditions. These could be as simple as a loss of efficiencies or as critical as mechanical failure.
Today, many of the main stream software companies today use both of these techniques in their software suites to access a variety of data point’s sources and apply a variety of mathematical and statistical formulas to discover the best decision for a given situation.
While most software developer’s offer advanced predictive-analytic software for identifying impending equipment problems and avoiding shutdowns or equipment failure. The difference in today’s predictive software’s can vary on how they leverages existing data, the modeling and infrastructure needed to provide early and actionable warnings of impending problems.
I recommend defining a long term strategy before choosing the software to deploy at your organization. Here are some examples that you should consider; improve your operations, optimize maintenance resources and procedures, maximize equipment performance, and avoid unexpected shutdowns and catastrophic failures, increase availability, ensuring regulatory compliance, reliability, and efficiency.
Also recognize that every piece of equipment is unique, your organization may be challenged with aging equipment, internal technical resources, limited budgets and most importantly lack of vision. Having a clear vision and organization support is critical to any long term sustainability. All of these questions need to be defined before purchasing your predictive analytic software.
Now that have a plan you can refine short list of software by functionality. Some examples are, do they create empirical models, does the software utilize templates for distribution across like assets, and does the software write its notification back to a data historian, Prophecy, PDH or OSIsoft PI System.
At the end of the day your Predictive analytic software integration should give your company a competitive edge and improve the ROI, substantially and ensure integrity. Predicative analytical software is becoming the science that removes guesswork out of the decision-making process and applies proven scientific guidelines to find right actions in the shortest time possible.