What are the most common types of failures and faults that predictive maintenance can detect and prevent?

Predictive maintenance is best applied to detect and identify potential failure in mechanical that randomly can happen. The potential failure is not related with a time frame. Technology available in the market is the extension of human sense. Previously we detect an issue with the equipment by touching it, if it vibrates extensively we know something was wrong. Today we can measure how much it vibrates and where is the sources of the vibration. Hence the commons sense of the technician to visually inspect the equipment condition during data collection should not be discounted.

The mechanical failure may have been caused by The life cycle of equipment (Time based, Run based, and Run to failure) and caused by the condition based on the equipment functional failure. Time based such as when the engine works specific Time we should be overhauling to keep the parts from deteriorating and damage. Run based such as when the machine produces the specific number of finishing goods we should inspect the machine to replace the defective parts. Run to failure such as lighting lamps. Condition based such as the corrosion in the impaler of the pump.

As a vibration analyst, I've pinpointed common mechanical failure conditions such as imbalance, misalignment, bearing defects and looseness. My thesis for my Mechanical Engineering degree, conducted while working at SETISA, summarized over 20 years of vibration analysis reports from the company. I found that bearing failures were the majority, often resulting from primary issues like those mentioned.

Mechanical problems and failures are generally the most common types for rotating equipment. Fan wheel unbalance, belt wear and/or misalignment, sheave wear and/or damage, bearing wear, coupling misalignment, lack of lubrication, and frame looseness or damage all can occur regularly with varying degrees of severity. Predictive maintenance allows you to use data collection and analysis to pick the best and most efficient time for repairs. This allows facilities and operators to maximize the lifespan of the or equipment and parts. Additionally, as a predictive maintenance program matures a baseline is established that helps inform specific facility decisions that don’t rely on industry averages.

Actually predictive maintenance equips you with the data and insights you need to make better decisions, target maintenance more precisely, and learn new things every day. This proactive approach transforms you from a reactive fix-it person into a problem solver, data expert, and machinery reliability champion. Ultimately, PdM benefits both your career and your organization's efficiency.

In my Master's project at the University of Siegen, I researched bearing damage detection using electrical current analysis. The findings revealed that even minor damages to bearing tracks could substantially alter the motor's electromagnetic field and, consequently, its current. This emphasizes the utility of electrical current analysis in identifying mechanical issues, thus narrowing the gap between mechanical and electrical maintenance practices. Lately, I have been working with electric transmission and distribution companies, identifying the most common electrical asset failures detected through predictive maintenance: insulation failure, overheating of conductors, insulation breakdown, and moisture.

From a physics perspective, most failures are the result of the physical process of reaching critical points in complex systems. Consequently, the success of predictive maintenance as a business model depends heavily on the data used to predict failures. This data must be consistent with the physical process underlying the operation of the equipment being monitored.

Predictive maintenance in can detect and prevent various operational failures, including sensor issues, communication faults, power supply problems, software glitches, mechanical failures, and calibration errors. This proactive approach ensures the reliability of machines, minimizing risks and enhancing safety.

A predictive maintenance plan that includes visual inspections alongside vibration analysis, thermography, ultrasound, and motor current analysis can effectively monitor operational parameters. This approach aids in early detection of deviations, such as obstructions in heat exchanger tubes or significant leaks in compressed air or steam systems. Long-term monitoring of these parameters can reveal deeper issues, enhancing operational maintenance effectiveness.

Operator is the first line of defense to prevent breakdowns. Their engagement to monitors any deviation occurs and corrected it or report it to maintenance department will greatly help the organization maintaining the asset at optimum condition.

Two main influences on what types of problems that can be detected by a predictive maintenance program are: 1) The technology used (Vibration, Ultrasound, Lubrication Analysis, Thermal Imaging, etc.). Are you using all of the available methods? 2) Predictive maintenance team experience and training. A high-quality program will detect and diagnose a multitude of problems. Vibration analysis teachers indicate that over 50 unique mechanical, electrical and control system problems can be identified in rotating machinery. That is just one technology! Process and quality problem diagnostics are a bonus if the team wants to dig into it. A higher degree of confidence in the diagnostics is found if 2 technologies indicate the same problem!

I cannot stress enough the need for vibration analysis with rotating equipment, but best practice comes from the five senses we already possess and our ability to process that simple data into an extrapolated time of failure.

Software failures are emerging as significant concerns, often overlooked by traditional reliability experts. The gap in timely detection and prevention of software issues is not adequately covered by predictive maintenance, IT services, or cybersecurity teams. In organizations with dedicated cybersecurity departments, it's crucial for these teams to handle software updates, log analysis, secure programming practices, and vulnerability assessments. This multidisciplinary approach ensures a comprehensive strategy for software early failure detection and prevention.

From my experience as a vibration analyst, I've observed a notable gap between the failure modes typically identified by consultants and the actual faults found in the field through condition monitoring techniques like vibration analysis, thermography, ultrasound and motor current analysis. This contrast highlights a significant distance between those who provide consultancy and those who work on field. It underscores the importance of integrating real-world data and hands-on insights into predictive maintenance strategies to bridge this gap and enhance accuracy in fault detection and prevention.

El mantenimiento predictivo es unos de los métodos más aplicables para detectar fallas en los equipos, va acompa?ado con el mantenimiento preventivo, reducir accidentes laborales es uno de las razones que se busca que el trabajador conozca cuando aplicarlo, la inspección visual, auditivo, etc ayuda a detectar comportamientos sospechosos del equipo, ya sea estas por vibraciones, malos olores, ruidos inadecuados etc. Un buen Mantenimiento a larga la vida útil del equipo y evita accidentes laborales.

Calibration Drift: Regular calibration checks can prevent inaccuracies in measurement devices like blood glucose monitors, ECG machines, and other diagnostic equipment.