Failure diagnosis in assets: Steps, tools, and best practices

Failure diagnosis in assets is an essential process to increase reliability and operational safety in industrial environments. When performed superficially or only as a corrective measure, the risk is addressing symptoms without tackling the root cause — leading to recurring failures, higher costs, and reduced productivity.

Just like in medicine — where a fever can indicate anything from a common cold to a severe infection — symptoms in industrial assets must be analyzed in depth. In this way, without a structured diagnosis, maintenance becomes reactive and exposes the plant to unplanned downtime.

In this article, we will explore how to perform an efficient diagnosis, which tools can support the process, and how technologies such as predictive maintenance and continuous asset monitoring enhance early anomaly detection.

What is failure diagnosis in assets?

Failure diagnosis in assets is the process of investigating and identifying the root cause of failures in industrial equipment. In this manner, more than repairing the visible defect, it aims to understand the technical, operational, or process-related factors that led to the problem, ensuring a lasting solution and preventing recurrence.

In practice, this means transforming symptoms — such as increased vibration, abnormal heating, or performance drop — into actionable information that guides well-founded corrective actions. Therefore, this process relies on historical records, technical analysis, and structured methodologies for equipment failure analysis, allowing maintenance to be not just reactive but strategic.

So, by adopting diagnosis as part of the maintenance routine, industries reduce unplanned downtime, improve asset availability, and strengthen reliability culture.

What are the steps in failure diagnosis?

Failure diagnosis in assets should follow a structured process that enables identifying causes, validating hypotheses, and implementing corrective actions to prevent recurrence. Otherwise, ignoring these steps risks treating only symptoms, leaving operations exposed to downtime and high costs.

Below are the main steps in this process:

1. Identify the source of the problem

The first step is to gather information that helps locate where the failure began. This includes:

• Occurrence record: Document the time of failure, observed symptoms, and operating conditions.
• Inspection history: Review previous reports, pre-use routines, and maintenance checklists.
• Observation on the shop floor (gemba): Talk to operators and technicians to identify abnormal behaviors before the failure.
• Procedure review: Check whether equipment usage or the maintenance plan may have contributed to the failure.

This step is essential to distinguish apparent symptoms from the actual root cause.

2. Structure the analysis and organize data

With the collected information, the next step is to consolidate data and turn it into technical hypotheses. Thus, this reduces the influence of intuitive decisions and increases the objectivity of equipment failure analysis.

Support tools such as “5 Whys” or the “Ishikawa (Fishbone) Diagram” help break down symptoms into potential causes, making it easier to define corrective and preventive actions.

3. Define an action plan and monitor results

After identifying the root cause, it is necessary to create a structured action plan to eliminate or mitigate the problem. This plan should include:

• Corrective action: Immediate solution to restore operation.
• Preventive action: Adjustments in procedures, training, or technical specifications to avoid recurrence.
• Monitoring: Define effectiveness indicators, such as increased MTBF, reduced MTTR, or stabilized vibration levels.

Results should be shared with the entire maintenance and operations team, consolidating a continuous learning cycle. In this proccess, insights obtained can also feed predictive maintenance programs, anticipating similar failures before they become critical.

Hence, with these three steps, failure diagnosis becomes part of strategic maintenance management, supporting reliability and continuous asset monitoring.

Tools for failure diagnosis in assets

To ensure technical and reproducible failure diagnosis, it is necessary to combine structured failure analysis methods with reliable operational data. Below are four tools that together form a robust set to find root causes, reduce unplanned downtime, and prioritize actions with the best cost–benefit ratio:

5 Whys

The 5 Whys analysis is an investigative tool that goes beyond visible symptoms to find the true cause of a failure. In this manner, its logic is straightforward: state the problem and, for each answer, ask “why?” again. The process repeats until reaching a point where the failure no longer depends on other factors or reveals a process, management, or maintenance issue that needs to be addressed at its root.

To apply it correctly in asset failure diagnosis, follow these steps:

• Clear problem definition: Record what happened, when it occurred, which asset was affected, and what symptoms were observed.
• First question: Identify the immediate cause of the event (e.g., the motor stopped because it overheated).
• Successive iteration: For each answer, repeat “why?” to deepen the analysis, connecting intermediate causes.
• Reach the root cause: Typically after four to six questions, you arrive at a structural factor, such as a maintenance plan failure, lack of inspection, or deviation in operational procedure.
• Validation and corrective action: Confirm the root cause with data (e.g., failure history, vibration measurements, inspection reports) and define an action to prevent recurrence.

For example:

This example shows that the root cause is not the motor failure but the absence of a preventive inspection plan for peripheral components. Applied this way, the tool avoids superficial repairs and generates permanent improvements in the maintenance process.

Fishbone Diagram (Ishikawa)

The Ishikawa diagram organizes root causes of recurring failures visually and logically. It classifies problem sources into major groups: Machine, Method, Manpower, Material, Environment, and Measurement (the “6Ms”).

This diagram is useful for:

• Identifying hidden causes of repetitive failures in critical equipment.
• Mapping variables that impact performance (such as inadequate lubrication or lack of calibration).
• Supporting action plans that eliminate the problem permanently instead of treating it superficially.

In addition, this method is especially effective when combined with techniques like Pareto analysis (80/20), which helps prioritize the causes that contribute most to failures.

The image below shows an example of the Ishikawa diagram applied in industrial contexts.

Fault Tree Analysis (FTA)

Fault Tree Analysis (FTA) starts with an undesired event, such as a circuit breaker failure, a critical motor shutdown, or a production line stoppage. And so, it then breaks down its causes into a hierarchical tree-shaped model.

The technique uses AND/OR logic gates to represent the relationship between basic failures and the main event. This makes it possible to:

• Quantify the probability of system failure.
• Identify single points of failure.
• Support decisions on redundancy, automation, and contingency plans.

Moreover, Fault Tree Analysis is widely used in high-criticality industries such as aviation, nuclear, oil and gas, and power generation, where reliability is directly linked to personnel safety and operational continuity.

How to improve diagnosis processes?

Failure diagnosis in assets should not be seen as a one-time activity but as part of a continuous improvement cycle. Thus, as collected data becomes more accurate and analysis processes more structured, asset reliability increases and the likelihood of recurring failures decreases. To achieve this, some practices are essential:

Standardize failure and symptom records

The foundation of a good diagnosis is the quality of the information collected. Therefore, every failure must be recorded consistently, including observed symptoms, operating conditions, date, time, and actions taken. Standardization prevents data gaps and facilitates comparison of occurrences over time. Additionally, maintenance management software (CMMS/EAM) plays an important role by centralizing these records and enabling faster, more reliable analysis.

Ensure traceability of maintenance actions

Diagnosing the failure is not enough; it is necessary to track the effectiveness of corrective and preventive actions. Traceability ensures that each intervention is linked to the event that originated it, allowing evaluation of whether the problem was truly resolved or merely postponed. This also supports internal and external audits and reinforces the reliability of critical asset history.

Train the team in analysis methodologies

Tools such as the 5 Whys are only effective when properly applied. For this, the maintenance team must be trained in failure analysis methodologies and in interpreting technical data (vibration, temperature, spectral analysis, oil, electrical energy). This way, training strengthens team autonomy and raises the maturity level of asset management.

Adopt continuous monitoring technologies to anticipate failures

Continuous monitoring through IIoT sensors enables detection of incipient failures before they become critical. Hence, these data, integrated into cloud platforms and analyzed by artificial intelligence algorithms, increase diagnostic accuracy and reduce reliance on manual inspections. For example, the Dynamox solutions ecosystem.

In practice, this translates into avoided unplanned downtime, greater operational reliability, and decisions based on indicators such as MTBF, MTTR, and ROI.

How does Dynamox support failure diagnosis in assets?

The Dynamox ecosystem was developed to transform failure diagnosis in assets into a digital, reliable, and data-driven process. Our solution combines IoT sensors, gateways, analytical software, and artificial intelligence, creating a complete approach for monitoring and analyzing industrial assets.

• DynaLogger Sensors: Continuously collect data on equipment condition, enabling detection of incipient failures that would hardly be noticed during manual inspections.
• DynaGateway: Automates data collection and transmission to the cloud, ensuring integrity and frequency of information without constant team intervention.
• Dynamox Platform: Centralizes all information in interactive dashboards, customizable reports, and configurable alerts, making trend visualization and evidence-based decision-making easier.
• DynaDetect: Applies advanced AI algorithms to identify failure patterns, suggest possible causes, and support the technical team in defining action plans.

Through this integration, Dynamox helps industries reduce unplanned downtime and strengthen critical asset management. Discover Dynamox solutions and see how to turn failure diagnosis into a continuous, intelligent, and sustainable process.

Frequently Asked Questions about failure diagnosis in assets (FAQ)

What is the difference between identifying and diagnosing a failure?

Identifying a failure means noticing an abnormal symptom in the equipment, such as increased vibration, overheating, or unusual noise. Diagnosing goes further: it investigates the root cause behind the symptom using monitoring data, maintenance history, and structured analysis tools. For example, identifying may be noticing high vibration in a motor; diagnosing is concluding, based on data, that the cause is rotor imbalance or bearing wear.

Does diagnosis replace predictive maintenance?

No. Diagnosis and predictive maintenance are complementary. Predictive maintenance acts proactively, monitoring variables such as vibration and temperature to predict failures before they occur. Diagnosis comes into play when a failure has already manifested, helping to understand why it happened and prevent recurrence. While predictive maintenance strengthens prevention, diagnosis strengthens learning and continuous improvement.

Which assets should be prioritized in structured diagnosis?

Structured diagnosis should start with critical assets — those whose failure directly impacts safety, production, and operating costs. Examples include pumps, motors, compressors, fans, and conveyor belts in sectors such as mining, pulp and paper, or food and beverage. Peripheral assets can also be analyzed, but the initial focus should be on machines that, when failing, cause unplanned downtime and significant losses.

How does Industry 4.0 impact failure diagnosis?

Industry 4.0 has transformed failure diagnosis into a continuous, data-driven process. IoT sensors collect information continuously; gateways automatically transmit this data; and cloud platforms apply Big Data and Artificial Intelligence to identify patterns and issue early alerts. Diagnosis no longer depends solely on human perception and becomes a digital, agile, and reliable process, supporting MTTR reduction, MTBF increase, and strategic decision-making in asset management.