predictive maintenance
The problem
Plants with critical rotating equipment, boilers and kilns, and operators of mining haul fleets, distribution and delivery fleets, and remote generator sites such as telecom towers.
A typical situation
Our maintenance is either calendar-based or reactive. One failure on a kiln fan, boiler feed pump or haul truck costs us days of output. On our fleet and generator sites, fuel use is well above what it should be, and we cannot tell whether that is driving behaviour, equipment condition or pilferage.
Outcomes
Indicative ranges, validated in your pilot. These figures come from comparable industry programmes, not from TashIT projects. Your pilot sets a baseline with you and measures the actual change.
Typically 20-40% less on monitored assets
Basis: Condition-based and predictive maintenance programmes on rotating equipment and fleets. Depends on asset criticality, failure modes and how quickly maintenance acts on alerts.
Typically 5-15% lower on monitored assets
Basis: Fewer emergency repairs and less secondary damage, with parts and labour planned ahead. Depends on the current maintenance strategy and spares practice.
Typically 5-12% less fuel
Basis: Telematics-based control of idling, routing, driver behaviour and generator run hours. Depends on existing fuel controls, fleet mix and duty cycles.
Typically days to weeks ahead for common rotating-equipment faults
Basis: Vibration and temperature trends for bearing, alignment, imbalance and lubrication faults. Sudden failures with no measurable precursor cannot be predicted.
From data to decision
A criticality review with your maintenance team looks at failure history, cost of downtime and spares lead time, and selects a focused set of assets or one fleet segment.
Historian and SCADA tags, existing condition-monitoring systems and CMMS work orders are connected, with wireless vibration and temperature sensors added where needed. For fleets: telematics, OEM data feeds, fuel cards and dispensing records.
Models learn each asset's normal operating envelope by mode and load, detect deviations and, where history allows, estimate remaining useful life. Engineering rules, such as ISO 20816 vibration severity limits, sit alongside machine learning.
Alerts carry the likely fault, the evidence and a recommended action, and can create work orders in your CMMS. For fleets and generator sites, fuel and idling exceptions are reported by vehicle, driver and site.
Technicians record what they found, which refines the models. A monthly review covers confirmed faults, false alarms and fuel trends.
Data and integration
How the pilot works
Duration
Scope
Agreed in writing before work starts, with targets set against your baseline.
The pilot follows a short diagnostic and ends with a written results review. Scaling up is a separate decision, made on the measured results. How our engagements work
Where it applies
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FAQs
Something else to ask?
Email your question with a little about your site and systems, and we will reply in plain language.
info@tashit.comNot always. Many plants already collect useful data in historians, protection relays and drives, and most modern vehicles report through telematics. Sensors are added only where critical assets have no usable signal.
Rule-based and anomaly alerts can work within weeks of connecting the data. Failure-specific predictions improve as the models see more operating history and maintenance feedback, which is why the pilot includes a feedback loop from the first week.
Yes. Data can be buffered on an edge device and sent when a link is available, and simple alerts can run locally. The design depends on the site's power and communications, which we survey first.
Tell us about your site or process and the data you already have. We will reply with a diagnostic plan, the measures we would propose for a pilot and an honest view of feasibility.