Workshops need to count thermocouple failure data regularly to optimize procurement brands, adjust maintenance cycles and reduce comprehensive spare parts and loss costs. Three mainstream statistical analysis methods are widely used: failure frequency classification statistics, service life cost-benefit comparison, and production load failure correlation analysis. This article compares the applicable scenarios, data acquisition difficulty and decision-making value of each method.
Method 1: Failure frequency classification statistics (basic daily management method)
Data collected: Monthly total failure quantity, quantity proportion of each fault type (drift, open circuit, short circuit, mechanical damage, wiring error).
Operation steps: Record fault code during each maintenance, summarize percentage of each fault category at month-end, draw fault proportion pie chart.
Decision application: If thermal drift accounts for over 40% of faults → upgrade to N-type anti-drift thermocouples and shorten carbon cleaning cycle; if open-circuit mechanical damage dominates → optimize wiring tension protection and vibration isolation measures.
Advantages: Simple data collection, low statistical labor cost, suitable for small and medium-sized workshops with limited data management personnel.
Disadvantages: Cannot quantify economic loss brought by different faults, only qualitative improvement guidance without cost numerical reference.
Method 2: Service life cost-benefit comparison (procurement supplier evaluation method)
Data collected: Each brand thermocouple unit purchase price, average service life, monthly maintenance labor hours consumed, average downtime loss caused by failure, defective product scrap loss.
Calculation index: Comprehensive daily cost of each brand = (procurement amortization cost + maintenance labor cost + downtime scrap loss) ÷ actual service days.
Decision application: Compare daily comprehensive cost of K-type domestic generic, high-end domestic alternative and imported N-type thermocouples; select the brand with the lowest daily comprehensive cost matching production load.
Advantages: Quantify all hidden losses into numerical cost, provide direct data basis for procurement budget adjustment and brand replacement.
Disadvantages: Data collection workload is large, needs MES system to record downtime and scrap quantity synchronously.
Method 3: Production load failure correlation analysis (maintenance cycle optimization method)
Data collected: Production load type (three-shift continuous / two-shift intermittent / single-shift trial), daily average molding cycle times, processing material corrosiveness, thermocouple average failure interval days.
Operation steps: Establish correlation curve between production load intensity and failure cycle, calculate the optimal differentiated calibration and cleaning cycle for each load mode.
Decision application: High-load three-shift corrosive material production line shortens calibration cycle to 30 days; low-load single-shift trial molds extend calibration cycle to 180 days, balance maintenance labor input and failure risk control.
Advantages: Realize refined differentiated maintenance management, avoid over-maintenance waste or under-maintenance quality risk.
Disadvantages: Long data accumulation cycle, need at least 3 months of continuous fault recording to form accurate correlation curve.
Workshop composite statistical application suggestion
Small workshops: Adopt Method 1 monthly fault classification statistics as daily management basis.
Medium-large mass production workshops: Combine Method 1 + Method 2, monthly fault classification + quarterly brand cost-benefit evaluation to adjust procurement and maintenance strategies.
Intelligent workshops with complete MES data collection: Superimpose Method 3 load correlation analysis to realize full digital refined thermocouple cost and risk control.
