Condition monitoring is sometimes portrayed in industry messaging as being effectively limited to vibration analysis and ultrasound. Such claims, including those recently advanced by emerging vendors in the sensing technology space, reflect a broader pattern of marketing-driven simplification rather than technical accuracy. International standards define condition monitoring as a process that incorporates multiple diagnostic techniques, including lubricant analysis, vibration, thermography, and electrical methods ¹.

Reliability-centered maintenance theory further emphasizes that different technologies are required to detect distinct failure modes and stages of degradation ². Empirical research in predictive maintenance consistently demonstrates that no single method provides complete fault coverage across all mechanical systems ³. These foundational perspectives establish that condition monitoring must be understood as an integrated, multi-technology framework rather than a simplified binary construct.

Condition monitoring must be understood as an integrated, multi-technology framework rather than a simplified binary construct.

Oil analysis is a critical component of this framework because it can detect early-stage degradation and identify the root causes of failure. Oil analysis is formally recognized as a primary condition monitoring method capable of evaluating lubricant condition, contamination, and wear debris as indicators of machine health ¹. Standardized testing methods developed by ASTM provide consistent procedures for assessing these parameters across a wide range of equipment types ⁷.

In particular, tribological research demonstrates that wear particle generation occurs at the onset of failure mechanisms, often preceding measurable changes in vibration signatures ⁴. Furthermore, applied studies in machinery diagnostics show that oil analysis frequently identifies incipient faults earlier than vibration-based methods in rotating equipment systems ⁶. Accordingly, this article directly challenges reductionist claims by demonstrating that condition monitoring is inherently multi-modal and that oil analysis plays a critical, often leading role in fault detection and diagnosis.

Condition Monitoring as a Multi-Technology Framework

Standards and Established Practice

Condition monitoring is fundamentally structured as a multi-technology discipline designed to capture different dimensions of machine degradation. ISO 17359 explicitly identifies lubricant analysis as a primary condition-monitoring technique, alongside vibration and other diagnostic methods ¹. Furthermore, reliability-centered maintenance frameworks reinforce that the selection of monitoring technologies must align with specific failure mechanisms rather than convenience or convention ².

In addition, industry research demonstrates that combining multiple technologies significantly improves fault detection accuracy and reduces the risk of missed failures ³. Taken together, these perspectives confirm that any attempt to reduce condition monitoring to a limited subset of technologies is inconsistent with established practice.

Any attempt to reduce condition monitoring to a limited subset of technologies is inconsistent with established practice.

This broader context underscores the need to examine how different technologies contribute uniquely to fault detection.

Failure Mechanism Complexity

The necessity of multiple technologies becomes more evident when considering the complexity of failure mechanisms in rotating equipment. Mechanical systems experience degradation through processes such as wear, fatigue, corrosion, and contamination ⁸. Notably, each process produces distinct physical and chemical signatures that are not uniformly detectable by a single monitoring method ⁵.

Moreover, empirical studies have shown that certain faults remain undetected when relying exclusively on vibration or acoustic methods ⁹. Consequently, this complexity reinforces—not merely suggests—the need for oil analysis within a comprehensive monitoring strategy.

Oil Analysis as a Foundational Diagnostic Method

Lubricant Condition and Wear Assessment

Oil analysis serves as a foundational diagnostic method by providing direct insight into both lubricant condition and machine wear. Standard practices defined by ASTM establish oil analysis as a structured approach for monitoring viscosity, oxidation, contamination, and wear metals ⁷. In particular, tribological research confirms that lubricants act as carriers of wear debris and contaminants, effectively transporting evidence of internal machine conditions ⁴.

Additionally, engineering studies demonstrate that oil analysis enables simultaneous evaluation of mechanical and chemical degradation processes ⁵. As a result, this dual capability distinguishes oil analysis from external sensing methods that rely solely on energy measurement. This distinction becomes particularly important when precise diagnostic resolution is required for effective maintenance decisions.

Characterizing Wear Mechanisms

The diagnostic depth of oil analysis is further enhanced by its ability to characterize wear mechanisms. Analytical ferrography and particle analysis techniques allow for the identification of wear modes such as abrasion, adhesion, and fatigue ⁹. Likewise, elemental spectroscopy provides additional resolution by linking detected metals to specific machine components ⁶.

Together, these capabilities enable practitioners to move beyond fault detection and toward precise failure diagnosis. Consequently, this level of diagnostic resolution establishes oil analysis as a critical tool for understanding the underlying causes of machine degradation.

Early Fault Detection and the Failure Progression Curve

Detection at the Point of Origin

Oil analysis enables earlier fault detection by identifying degradation at the point of origin within the machine. Wear particles are generated during the initial stages of material interaction, often before significant energy is produced ⁴.

In fact, empirical studies have shown that wear debris analysis can detect bearing and gear faults months in advance of vibration-based detection thresholds ⁶. In applied industrial settings, advanced lubricant data analysis has demonstrated the potential to extend this detection window significantly, in some cases approaching multiple years of advanced indication.

Wear debris analysis can detect bearing and gear faults months in advance of vibration-based detection thresholds.

Furthermore, additional research indicates that early-stage contamination and lubricant degradation can be identified before they result in measurable mechanical symptoms ⁵. As a result, these findings demonstrate that oil analysis operates at the earliest portion of the failure progression curve. This early positioning is best understood within the context of reliability engineering models.

The P–F Interval Advantage

The temporal advantage of oil analysis is best understood within the context of the P–F interval. Reliability literature defines the P–F interval as the time between detectable potential failure and functional failure ². Importantly, technologies that detect faults earlier within this interval provide a greater opportunity for corrective action and risk mitigation ³.

In contrast, comparative studies have shown that vibration analysis often detects faults at later stages when damage has progressed sufficiently to affect machine dynamics ⁹. Therefore, this positioning highlights the strategic value of oil analysis in extending the predictive maintenance window.

Root Cause Identification and Diagnostic Resolution

Particle Morphology and Elemental Analysis

Oil analysis provides diagnostic resolution that enables the identification of root causes of failure. Wear particle morphology allows analysts to distinguish between different wear mechanisms based on particle size, shape, and texture ⁴. Elemental analysis further supports root cause identification by associating specific metals with machine components ⁶.

Contamination analysis reveals external influences such as dirt ingress or water contamination that contribute to accelerated wear ⁵. These capabilities allow oil analysis to move beyond symptom detection and toward causal diagnosis. This ability to identify underlying causes has direct implications for maintenance effectiveness.

From Symptoms to Causes

The ability to identify root causes has significant implications for maintenance strategy. Corrective actions based on root cause analysis are more effective than those based solely on symptom detection ³. Studies in reliability engineering have demonstrated that addressing underlying causes reduces recurrence rates and improves equipment lifespan ².

Corrective actions based on root cause analysis are more effective than those based solely on symptom detection.

In contrast, technologies that primarily detect symptoms may require additional analysis to determine the source of failure ⁹. This distinction reinforces the importance of oil analysis within a comprehensive diagnostic framework.

Internal Access Versus External Measurement

The Lubricant as a Diagnostic Medium

Oil analysis provides direct access to the internal operating environment of machinery. Lubricants circulate through critical components, collecting information about wear, contamination, and chemical changes ⁵. As such, tribological studies confirm that this internal perspective allows for the detection of conditions that are not immediately observable through external measurement ⁸.

Moreover, wear debris transported in the lubricant reflects real-time interactions occurring at the surface level of machine components ⁴. Consequently, this internal visibility provides a unique diagnostic advantage. This advantage becomes more apparent when contrasted with external sensing approaches.

Limitations of External Sensing

External sensing technologies, including vibration and ultrasound, rely on detecting energy transmitted through machine structures. By comparison, these methods require faults to reach a severity level that produces measurable signals ⁹. Additionally, signal interpretation can be influenced by factors such as machine geometry and operating conditions ³.

As a result, certain early-stage faults may remain undetected until they progress further. This contrast highlights the complementary nature of internal and external monitoring approaches, particularly when evaluating the limitations of any single diagnostic method.

Limitations of Vibration and Ultrasound as Exclusive Solutions

Detection Gaps in Single-Technology Approaches

Vibration and ultrasound are valuable diagnostic tools, but are limited when used as standalone solutions. Vibration analysis is highly effective for detecting imbalance, misalignment, and looseness, but is less sensitive to early-stage wear in low-energy conditions ³. Ultrasound can detect friction-related phenomena but provides limited information regarding wear mechanisms and contamination sources ⁹.

Research has shown that reliance on a single technology increases the likelihood of missed or delayed fault detection ². These limitations underscore the need for a multi-technology approach. This recognition leads directly to the importance of integrating complementary diagnostic methods.

Closing the Gap with Oil Analysis

The integration of oil analysis addresses many of these limitations by providing complementary data. Oil analysis captures early-stage degradation and identifies root causes, while vibration and ultrasound provide information about fault severity and dynamic behavior ⁵.

Studies in predictive maintenance demonstrate that combining these methods improves diagnostic accuracy and maintenance decision-making ³. This integrated approach aligns with best practices in reliability engineering and forms the basis for modern condition monitoring strategies.

Integration of Technologies in Modern Reliability Practice

Complementary Diagnostics as Core Principle

Modern reliability practice formalizes the integration of multiple condition monitoring technologies as a core operational principle. Reliability-centered maintenance frameworks advocate for the use of complementary diagnostic tools to address different failure modes ². Industry research demonstrates that integrated monitoring programs achieve higher reliability and lower maintenance costs than single-technology approaches ³.

Tribological and mechanical studies confirm that combining internal and external monitoring methods provides a more complete understanding of machine condition ⁸. These findings support a holistic approach to condition monitoring. This holistic approach is essential for maximizing diagnostic effectiveness.

Building a Comprehensive Diagnostic System

The integration of oil analysis with vibration and ultrasound creates a comprehensive diagnostic system. Oil analysis provides early detection and root cause identification, while vibration and ultrasound assess fault progression and severity ⁵. This combination enables more informed maintenance decisions and reduces the risk of unexpected failures ⁹.

Integrated monitoring programs achieve higher reliability and lower maintenance costs than single-technology approaches.

The resulting synergy enhances both detection capability and diagnostic accuracy. This integrated perspective provides the foundation for evaluating reductionist claims.

Conclusion

The assertion that condition monitoring has been reduced to vibration analysis and ultrasound is inconsistent with established standards, empirical research, and practical application. Oil analysis is recognized as a primary condition-monitoring technology in international standards and provides unique capabilities for early fault detection and root-cause identification ¹. Furthermore, tribological and engineering research demonstrates that oil analysis detects degradation at its origin and offers diagnostic insights not available through external sensing methods ^4,5.

In addition, reliability frameworks confirm that effective condition monitoring requires integrating multiple technologies rather than relying on a single approach ². Therefore, a more accurate and defensible position is that condition monitoring is a multi-technology discipline in which oil analysis plays a critical and often leading role in identifying and diagnosing machine failure.

To suggest otherwise is misleading and reflects a reductionist narrative that prioritizes market positioning over technical accuracy.

Growth strategies that narrow the scope of condition monitoring do not improve reliability; rather, they dilute it.

As industry leaders, we have a responsibility to represent these technologies accurately and with integrity, ensuring that end users are equipped with the full range of tools necessary to detect, diagnose, and prevent failure. Ultimately, anything less is a disservice to the profession and the organizations that depend on it.

References

1. ISO. (2018). ISO 17359: Condition monitoring and diagnostics of machines—General guidelines.
2. Moubray, J. (1997). Reliability-Centered Maintenance.
3. Bloch, H. P., & Geitner, F. K. (2014). Machinery Failure Analysis and Troubleshooting.
4. Stachowiak, G. W., & Batchelor, A. W. (2014). Engineering Tribology.
5. Totten, G. E. (2006). Handbook of Lubrication and Tribology.
6. Macian, V., et al. (2003). Wear, 255, 1297–1305.
7. ASTM International. (2020). Standards for used oil analysis and condition monitoring.
8. Hutchings, I. M., & Shipway, P. (2017). Tribology: Friction and Wear of Engineering Materials.
9. Anderson, D. (2012). Oil Analysis Solutions.

In the complex world of industrial machine maintenance, ensuring the health and longevity of industrial equipment is paramount. Industrial oil analysis plays a crucial role in this endeavor, providing valuable insights into the condition of machinery and the quality of lubricating oil.

However, a significant challenge lies in bridging the gap between the laboratory analysis reports and the end-users understanding of their machinery. In this article, we look into the critical importance of end-user knowledge in data interpretation, shedding light on the nuances of sampling, testing, alarms, and key points in understanding and utilizing oil analysis data.

Oil Sampling: The Foundation of Reliable Analysis

Sampling is the first step in oil analysis and lays the foundation for accurate and reliable data interpretation. End-users must comprehend the significance of proper sampling techniques and the importance of representative samples.

Ensuring that samples are taken at the right location, time, and with the appropriate equipment is vital. A small oversight in the sampling process can introduce inaccuracies that may lead to misinterpretations.

Here are some key points to consider regarding proper sampling techniques:

Sampling Location

Choosing the right sampling location is crucial. It should be representative of the oil’s condition throughout the system. Consider factors such as oil flow patterns, areas prone to contamination, and points where oil degradation is likely to occur. Reviewing OEM manuals or seeking expert guidance can help identify the most appropriate sampling points.

Sampling Frequency

Sampling frequency depends on several factors, including the criticality of the machine, the type of equipment, and the operating conditions. General guidelines recommend regular sampling at consistent intervals to establish trends and identify potential issues.

However, specific machinery or processes may require more frequent or periodic sampling to capture variations effectively.

While most routine sampling intervals for industrial equipment are set every quarter, monthly sampling can significantly increase the probability of early problem detection. This will ensure that the oil analysis technology receives the credit it deserves as an early indicator of machine health.

Sampling Equipment

Appropriate sampling equipment ensures sample integrity. Clean and properly sealed sample bottles, tubes, or syringes should be used to prevent contamination.

Different equipment may be necessary for different types of machinery, such as suction tubes, sampling valves, or dedicated sampling ports.

Sample Volume

Sufficient sample volume is necessary for accurate analysis. Following the laboratory’s guidelines regarding the required sample volume is important.

Insufficient volume may yield inconclusive results, while excessive volume can dilute contaminants or introduce inaccuracies due to a lack of appropriate headspace in the bottle for agitation. A general rule of thumb is to fill the sample bottle ¾ full.

Sample Collection Techniques

Proper techniques should be employed during sample collection to maintain sample integrity. Avoid touching the inside of the sample bottle or exposing it to contaminants.

Samples should be collected during normal operating conditions to obtain representative oil samples. Sample bottle lids should not be placed in a pocket or on the ground where they can collect contaminants during the sampling process.

Sample Labeling and Documentation

Accurate sample labeling and documentation are crucial for traceability and maintaining an organized record. Clearly label each sample with relevant information such as sampling date, machinery identification, sampling point, and other pertinent details.

Proper documentation allows for effective tracking of trends and comparisons over time. This process can be enhanced by properly using barcode labels and filling out the sample information in an online portal for sample registration.

Sample Handling and Storage

Handle oil samples carefully to prevent contamination or degradation. Store samples in clean, dry, and sealed containers to maintain their integrity during transportation to the laboratory. Samples collected during the week should be sent out by the end of the week. Failure to send samples out promptly can result in missed opportunities.

Remember, the quality and accuracy of oil analysis results heavily depend on the reliability of the sample collected. By adhering to proper sampling techniques, end-users can ensure that the samples accurately represent the condition of the machine and lubricant, enabling more meaningful data interpretation and effective maintenance decisions.

Testing: Unlocking the Secrets of Machinery Health

Once samples are collected, they undergo a battery of tests in the laboratory. The test results provide invaluable insights into various aspects of machine health, including the condition of the lubricant, the presence of wear debris, and the detection of contaminants.

While most laboratories excel at providing quality data, the challenge arises when laboratories present these results in technical jargon and generic comments, often detached from the intricacies of the specific machinery being analyzed. Some of the most common tests performed on oil samples include:

• Viscosity: Viscosity is a fundamental property of oil that indicates its resistance to flow. Viscosity testing helps assess oil’s ability to lubricate effectively and detect potential issues such as oil degradation or contamination. Deviations from the expected viscosity range may indicate problems with the oil’s or machinery’s operating conditions.
• Total Acid Number (TAN) and Total Base Number (TBN): At the most basic level, TAN and TBN tests measure the acidity and alkalinity of the oil, respectively. TAN determines the level of acidic contaminants, such as oxidation by-products, while TBN indicates the oil’s reserve alkalinity to neutralize acids. Monitoring TAN and TBN helps assess oil degradation, the presence of contaminants, and the effectiveness of the oil’s additives.
• Elemental Analysis: Elemental analysis, most commonly performed using inductively coupled plasma (ICP) spectroscopy, identifies and quantifies the concentration of various metals present in the oil. Elevated levels of specific metals, such as iron, copper, or lead, may indicate abnormal wear, corrosion, or the presence of contaminants in the machinery. Care should be taken with results from elemental analysis as the data reported is only related to particles less than 5 microns in size. Particles of this size are most likely considered to be in the “normal” wear debris size range. More to come on this topic in a later article.
• Particle Counting: Particle counting measures the concentration and size distribution of solid particles suspended in the oil. It helps detect abnormal wear, contamination, or the effectiveness of filtration systems. High particle counts or a significant increase in particle size can indicate potential machinery issues that require attention when there is a corresponding increase in elemental results.
• Water Content: Water can enter the lubricating oil through various means, including condensation, leaks, or improper maintenance practices. Measuring water content is essential, as excessive moisture can lead to oil degradation, corrosion, and reduced lubrication performance. Techniques like Karl Fischer titration are commonly employed to determine the water content in oil samples. It should be noted that if water target levels are below 500ppm (and they should be for anything critical enough to be on a routine sampling regime), the hot plate test offered by many laboratories is insufficient. Karl Fischer titration should be the standard test rather than an exception test.
• FTIR Spectroscopy: FTIR spectroscopy analyzes the oil’s molecular composition by measuring its absorption of infrared light. It can identify contaminants, oxidation by-products, additive depletion, and degradation products. FTIR spectroscopy is a versatile tool for detecting and diagnosing oil and machinery-related issues.
• Ferrography: Ferrography involves analyzing wear debris suspended in the oil. The technique uses magnets and filters to separate and analyze particles, providing valuable insights into the type, size, and severity of wear occurring within the machinery. Ferrography helps identify specific wear mechanisms and assists in diagnosing potential failures.

These are just a few examples of the many tests performed during routine oil analysis. Each test contributes to understanding different aspects of the oil’s condition and the machinery’s health.

By monitoring these parameters over time, end-users can identify trends, establish baseline values, and detect abnormalities, enabling proactive maintenance and optimization of machine performance.

Oil Analysis Alarms: Navigating the Sea of Alerts

Oil analysis often incorporates an alarm system that generates alerts when certain parameters deviate from acceptable levels. Alarms in oil analysis serve as early warning systems, alerting end-users to deviations or abnormalities in specific parameters that could indicate potential issues with the machinery or lubricating oil.

These alarms are typically based on predefined threshold values set by equipment manufacturers, industry standards, or specific guidelines tailored to the monitored machinery. An alarm is triggered when a monitored parameter exceeds or falls below the established threshold, signaling the need for further investigation or immediate action. Some important details about alarms used in oil analysis include:

• Alarm Types: Alarms in oil analysis can be categorized into warning and critical alarms. Warning alarms are triggered when a parameter approaches a predefined cautionary limit, indicating a potential issue that requires attention but may not demand immediate action. On the other hand, critical alarms indicate a significant deviation from the normal range and necessitate immediate investigation and appropriate maintenance measures to avoid severe damage or failure.
• Trend-Based Alarms: In addition to threshold-based alarms, trend-based alarms are also employed. These alarms monitor the rate of change in parameter values over time rather than a fixed threshold. Trend-based alarms detect abrupt or rapid parameter changes, indicating abnormal conditions or sudden deterioration that may not be captured by static thresholds alone.
• Customized Alarms: While manufacturers often provide standard alarm limits, end-users must customize the alarm settings based on their specific machinery and operational requirements. Factors such as equipment design, application demands, and historical performance data should be considered to establish appropriate alarm thresholds for each monitored parameter.
• Alarm Response and Action: When an alarm is triggered, end-users must respond promptly and appropriately. This typically involves initiating a thorough investigation into the root cause of the alarm, conducting additional tests or analyses, and consulting with experts or maintenance personnel to determine the necessary actions. The response can range from adjusting operating conditions, performing maintenance tasks, scheduling repairs, or conducting further diagnostic tests to identify the exact issue.
• Documentation and Analysis: Alarms and their corresponding actions should be properly documented for future reference and analysis. This documentation helps track the frequency and nature of alarms, monitor the effectiveness of maintenance actions, identify recurring issues, and refine alarm thresholds or response actions over time.

Alarms in oil analysis play a vital role in proactive maintenance, allowing end-users to address potential machine issues before they escalate into costly failures.

By monitoring critical parameters and responding to alarms effectively, end-users can mitigate risks, optimize equipment performance, and extend machinery lifespan. Regular analysis of alarm trends and continuous improvement in alarm protocols enhance maintenance strategies and overall machinery health.

Key Points in Data Interpretation: Beyond Generic Comments

The crux of effective data interpretation lies in moving beyond the generic comments provided by the laboratory. End-users must strive to understand the contextual relevance of the reported values within the specific machinery’s operating parameters.

This involves knowledge of the machinery’s design, operating conditions, maintenance history, and critical performance indicators. By digging deeper into the data, end-users can unveil hidden trends, patterns, and potential problems, enabling informed decision-making regarding maintenance and operational strategies.

Simply relying on laboratory comments is rarely the right decision when determining machine conditions or appropriate corrective actions.

The comments from the laboratory will mostly likely be canned comments developed by basic “if/then” statements that do not take into account the machine being monitored and most certainly not taking into account data that may be available to an end-user from other condition monitoring technologies, such as vibration analysis, ultrasound, and infrared monitoring.

Empowering End-Users: The Path to Enhanced Machinery Health

To bridge the gap between laboratories and end-users, collaborative efforts are essential. Laboratories should be encouraged to provide clear, concise reports that align with the end user’s understanding of their machinery.

Simultaneously, end-users must proactively seek knowledge and education about oil analysis techniques and data interpretation. Engaging with experts, attending training programs, and leveraging industry resources can empower end-users to become more proficient in deciphering the intricacies of oil analysis reports.

I recommend that end-users wanting to understand more about proper data interpretation seek training courses that focus on reality-based material rather than theoretical data evaluation.

Industrial oil analysis holds immense potential for optimizing machinery health and mitigating costly downtime. However, this potential can only be fully realized when end-users possess the knowledge and skills to interpret the data accurately.

By emphasizing the importance of proper sampling techniques, understanding test results, navigating alarms, and data interpretation, end-users can unlock the true power of oil analysis.

Collaboration between laboratories and end-users is key to ensuring data interpretation is approached with clarity and contextual understanding, paving the way for enhanced machinery performance, longevity, and operational efficiency.

Although management and leadership are different ideas, they are frequently used interchangeably. While the two have some parallels, their focus, abilities, and philosophies differ.

Understanding even the basics of management and leadership can help organizations determine how they want to approach the concept of lubrication-driven reliability.

Planning, organizing, directing, and controlling resources to accomplish particular goals or objectives is the process of management. Achieving a common purpose entails directing and coordinating the actions of a group of people.

The focus is on upholding the status quo and reaching predetermined goals through effectively and efficiently utilizing available resources. Usually having formal power over their employees, managers prioritize keeping consistency, minimizing risk, and accomplishing predetermined objectives. This idea is frequently used in the maintenance and industrial sectors concerning program management for lubrication.

Simply put, lubrication program management ensures that lubrication is carried out according to a set of predetermined KPIs. As a result, these managers typically adopt one of two dominant management philosophies: transactional management, where a manager sets clear expectations and goals and permits feedback, rewards, or punishment based upon employee performance towards those goals, or autocratic management, where the manager makes all the decisions and exerts total control over employees, expecting them to comply without question.

With any of the two major management approaches, employees are sometimes resigned to following instructions and are not necessarily encouraged to come up with fresh ideas.

Furthermore, the program manager is not encouraged to seek different ways of performing the work. It is often a process already in place with an expectation of execution with minimal variance in what is done or how. They are simply tasked with managing the existing process.

To create a true transformation in how lubrication is perceived and performed, it’s time to move from lubrication management to lubrication leadership.

Leadership is the ability to inspire and motivate people to achieve a shared vision or goal. It involves setting a direction for the organization or group and inspiring people to follow it. The emphasis is on innovation, creativity, and change.

Leaders often use their influence to achieve their objectives. They inspire and empower their team, take calculated risks, and drive change and growth. Generally, we think of leadership as someone at the top level of an organization or even a location. This is supported by common phrases such as “corporate leadership” or “site leadership.”

However, when understanding the previous description of a leader, we realize that the leader isn’t necessarily the one at the top. Instead, it’s the one that inspires. Creating a true transformation in lubrication-driven reliability takes someone with vision, charisma, and a genuine desire to influence change.

As with management, there are several leadership styles or theories, many of which overlap in many ways. However, in the world of lubrication leadership, the dominant style that comes to mind shares the namesake of what we are trying to execute- transformation.

Transformational Leadership

The transformational leadership approach focuses on inspiring and motivating others to achieve a shared vision or goal by empowering employees to take ownership of their work and encouraging them to be creative and innovative and to take calculated risks.

Transformational leaders are charismatic and have a strong vision for the organization’s future, and they inspire their followers by setting high standards and challenging them to meet those standards.

When the transformational leader focuses on programs such as lubrication, we can expect to see significant changes in what is done, how it is done, and finally, learn why it is done. The transformational leader will ensure that employees get that critical information often missed in today’s transactional approach: the “why.”

In short, we’ve often considered lubrication processes as something we manage. When we manage something, we ensure that tasks are performed as prescribed and focus on KPI tracking. We work towards numbers.

However, when we look at lubrication as something we lead, we open up the opportunity for innovation. We empower employees to want to do better. We reward them for finding ways to make their job easier and more exciting. There will always be a place for managers, but now is the time for leaders.