Industrial parts washers often operate silently: they do their job cycle after cycle, until one day something changes. Times get longer, results become less consistent, consumption increases, filters clog more often. At that point, maintenance becomes urgent, and machine downtime becomes a problem in the worst possible way: unplanned.
Predictive maintenance was created to avoid this scenario. It doesn't require technological revolutions, but a management approach that starts from already available data (even simple ones) and leads to more timely and targeted decisions. In this article, we'll look at how to apply it to your business.
Post-failure intervention. This is the most costly in terms of plant downtime and the knock-on effect (delays, rework, urgent spare parts).
Intervention based on timeframe (operating hours, number of cycles, calendar). This reduces breakdowns, but can lead to unnecessary maintenance and doesn't always detect non-standard problems (variable contaminants, different loads, actual usage).
Intervention based on actual machine and process conditions: signals and trends are observed to predict when performance begins to degrade. This is the approach that condition monitoring programs formalize at a general level (ISO 17359 guidelines).
A parts washer rarely goes from perfect to down without warning: your goal is to learn to identify the signs. The most useful ones fall into four categories:
These deviations often anticipate problems with filtration, thermal efficiency, losses, non-optimized cycles.
They are simple indicators, useful even without sensors: cycle counters and a time log are enough.
Here, predictive maintenance is linked to quality: loss of performance is often a symptom of mechanical problems (clogged nozzles, failing pumps), chemical problems (degraded bath) or management problems (incorrect load).
ISO 17359 cites typical parameters for condition monitoring (vibrations, temperature, flow rates, contamination, power) precisely because they are correlated to the real conditions of the machines.
In many applications, the stability of the washing bath is as important as the mechanics. Some practical signs of suboptimal operation are:
Predictive management, here, means avoiding calendar-based replacements and moving to conditional replacements, defined on simple thresholds (hours/cycles + performance indicators and/or internal tests).
Predictive maintenance works if it's light. The best method is a three-level progression..
Ideal for starting immediately:
To give a practical example: if the average cycle duration increases by 10-15% for three days, a quick check is opened: filters, nozzles, parameters, pump status.
The best thresholds look at the trend, not the isolated peak. For example:
Evaluating processing trends avoids false alarms and makes the alert credible.
This is where dashboards, telemetry, additional sensors, CMMS, or IIoT platforms come in. The goal remains simple: the same department language with a few well-chosen indicators.
A useful principle, taken from the world of industrial maintenance, is to scale the project only after a well-defined pilot phase (clear KPIs, ownership, routines)..
Once the signals and thresholds have been defined, a basic response table is needed. Here are some examples:
A few checks, in a logical order, with evidence of the results.
The most obvious impact is the reduction of unplanned downtime and emergencies. At the industrial level, many analyses report recurring benefits: less downtime and lower maintenance costs thanks to targeted and planned interventions. In practice, for a parts washer, the advantages translate into:
Effective predictive maintenance starts with a foundation: machines configured to work repeatably and ready to collect useful information.
At Geicos, we design customized industrial parts washers with a focus on robustness, accessibility, and process management. We also offer integrations and monitoring (including through third-party technologies) to simplify signal reading and operating threshold setting.
During the start-up phase, we can also provide specific training on the correct use of the machine, with operational guidance on parameters, controls, inspection routines, and best practices for maintaining consistent performance over time. This is the fastest way to transform data and signals into departmental habits that reduce unexpected events and downtime.